chooseGCM timespans

This Rmarkdown is part of the following article:

Esser, L.F., Bailly, D., Lima, M.R., Ré, R. 2024. chooseGCM: a toolkit to select General Circulation Models in R. In prep.

Introduction

chooseGCM is a solution for GCMs selection in Climate Change research. We built this Rmarkdown as a way to test the properties of the methods underlying chooseGCM. Results from each function will be presented side by side with changing variables, allowing better comparison.

This RMarkdown was built upon tests made from reviewer 1 to inform regarding timespans to each function. The only function we will not be covering is the , which takes a lot of time to download data and the timespan depends majorly on the internet connection and not on the coding optimization.

library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.3     ✔ tidyr     1.3.1
## ✔ purrr     1.0.2     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(sf)
## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE
library(chooseGCM)
library(tictoc)

10 min ——————————————————————

import —-

tictoc::tic()
s <- chooseGCM::import_gcms(path = "~/storage/WC_data/WC_data_all_gcms_10")
tictoc::toc()
## 0.184 sec elapsed
s
## $ac_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ac_ssp585_10_2090.tif 
## names       :  bio1, bio2,   bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -1.7, -103.3,    9.4, -24.7, -65.3, ... 
## max values  :  38.1, 21.5,   93.9, 2242.1,  56.3,  30.4, ... 
## 
## $ae_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ae_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.5, -1.0, -6.3,   11.5, -25.2, -67.5, ... 
## max values  :  36.8, 21.3, 94.1, 2229.1,  55.5,  29.1, ... 
## 
## $cc_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cc_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -45.1, -1.9, -28.6,   15.2, -22.8, -65.0, ... 
## max values  :  39.8, 21.4,  94.6, 2222.1,  58.3,  30.9, ... 
## 
## $ce_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ce_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.2, -1.1, -12.0,   13.9, -25.4, -67.5, ... 
## max values  :  37.0, 22.4,  94.8, 2282.1,  55.5,  29.2, ... 
## 
## $ch_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ch_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.9, -2.4, -33.1,   15.3, -23.3, -67.5, ... 
## max values  :  37.6, 22.0,  92.9, 2244.5,  58.4,  29.7, ... 
## 
## $cn_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cn_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -3.0, -34.3,   14.4, -23.7, -66.6, ... 
## max values  :  37.6, 22.2,  94.8, 2158.7,  56.7,  29.7, ... 
## 
## $cr_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cr_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.7, -16.9,    9.3, -24.3, -67.8, ... 
## max values  :  37.2, 22.3,  95.3, 2178.1,  55.9,  29.6, ... 
## 
## $ec_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ec_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.6, -2.6, -17.2,   11.9, -26.0, -68.5, ... 
## max values  :  37.6, 21.3,  95.8, 2355.0,  55.6,  30.0, ... 
## 
## $ev_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ev_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.9, -2.4, -13.4,   11.6, -26.2, -67.1, ... 
## max values  :  37.5, 21.3,  95.9, 2355.1,  55.5,  29.3, ... 
## 
## $fi_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : fi_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.3, -2.0, -17.9,   12.4, -24.6, -66.7, ... 
## max values  :  36.8, 21.3,  94.5, 2189.6,  54.8,  28.9, ... 
## 
## $gg_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gg_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.1,  0.0,    0,    7.2, -24.8, -66.2, ... 
## max values  :  36.8, 21.5,   96, 2266.3,  54.8,  28.4, ... 
## 
## $gh_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gh_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.7, -0.1, -0.3,    8.6, -25.1, -65.9, ... 
## max values  :  37.1, 22.6, 95.2, 2253.2,  55.4,  28.5, ... 
## 
## $hg_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : hg_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -2.4, -71.3,   11.8, -24.9, -66.6, ... 
## max values  :  38.4, 21.1,  95.2, 2287.1,  56.9,  30.8, ... 
## 
## $ic_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ic_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.0, -2.2, -14.9,   11.2, -25.6, -69.1, ... 
## max values  :  35.1, 22.1,  94.5, 2320.0,  52.5,  26.7, ... 
## 
## $in_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : in_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.5, -1.5, -17.2,    6.3, -25.4, -67.1, ... 
## max values  :  35.4, 22.6,  94.6, 2336.0,  53.3,  27.6, ... 
## 
## $ip_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ip_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -1.9, -13.2,    9.7, -25.8, -65.5, ... 
## max values  :  37.8, 21.2,  94.3, 2188.0,  56.2,  29.5, ... 
## 
## $me_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : me_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.7, -0.9, -4.1,   10.5, -27.2, -69.2, ... 
## max values  :  36.3, 22.5, 94.5, 2238.5,  54.6,  28.1, ... 
## 
## $mi_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mi_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4, bio5,  bio6, ... 
## min values  : -50.3, -1.9, -10.6,   13.4,  -26, -68.4, ... 
## max values  :  36.3, 22.5,  95.4, 2250.5,   55,  27.8, ... 
## 
## $ml_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ml_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.3, -0.7, -3.8,   11.9, -26.5, -68.8, ... 
## max values  :  35.0, 21.1, 95.4, 2249.7,  54.2,  27.4, ... 
## 
## $mp_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mp_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.8, -0.5, -2.9,   14.7, -26.3, -67.1, ... 
## max values  :  34.9, 21.1, 95.4, 2278.1,  53.5,  27.7, ... 
## 
## $mr_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mr_ssp585_10_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.6, -1.3, -8.1,   15.6, -24.3, -68.3, ... 
## max values  :  36.4, 21.9, 94.4, 2149.6,  55.0,  28.2, ... 
## 
## $uk_ssp585_10_2090
## class       : SpatRaster 
## dimensions  : 1080, 2160, 19  (nrow, ncol, nlyr)
## resolution  : 0.1666667, 0.1666667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : uk_ssp585_10_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.6, -2.8, -49.7,   10.5, -24.1, -66.6, ... 
## max values  :  38.7, 20.8,  94.6, 2324.7,  57.3,  30.6, ...
names(s)
##  [1] "ac_ssp585_10_2090" "ae_ssp585_10_2090" "cc_ssp585_10_2090"
##  [4] "ce_ssp585_10_2090" "ch_ssp585_10_2090" "cn_ssp585_10_2090"
##  [7] "cr_ssp585_10_2090" "ec_ssp585_10_2090" "ev_ssp585_10_2090"
## [10] "fi_ssp585_10_2090" "gg_ssp585_10_2090" "gh_ssp585_10_2090"
## [13] "hg_ssp585_10_2090" "ic_ssp585_10_2090" "in_ssp585_10_2090"
## [16] "ip_ssp585_10_2090" "me_ssp585_10_2090" "mi_ssp585_10_2090"
## [19] "ml_ssp585_10_2090" "mp_ssp585_10_2090" "mr_ssp585_10_2090"
## [22] "uk_ssp585_10_2090"
names(s) <- gsub("_ssp585_10_2090", "", names(s))
names(s)
##  [1] "ac" "ae" "cc" "ce" "ch" "cn" "cr" "ec" "ev" "fi" "gg" "gh" "hg" "ic" "in"
## [16] "ip" "me" "mi" "ml" "mp" "mr" "uk"

variable names and study area —-

var_names <- c("bio5", "bio13", "bio15")

study_area_parana <- geodata::gadm(country = "Brazil", path = "input_data/") %>% 
  sf::st_as_sf() %>% 
  dplyr::filter(NAME_1 == "Paraná")
study_area_parana
## Simple feature collection with 1 feature and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -54.61602 ymin: -26.71712 xmax: -48.02354 ymax: -22.5163
## Geodetic CRS:  WGS 84
##      GID_1 GID_0 COUNTRY NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1
## 1 BRA.16_1   BRA  Brazil Paraná      <NA>      <NA> Estado     State <NA>
##   HASC_1 ISO_1                       geometry
## 1  BR.PR  <NA> MULTIPOLYGON (((-52.52423 -...
plot(study_area_parana$geometry)

compare —-

tictoc::tic()
res10 <- chooseGCM::compare_gcms(s, var_names, study_area_parana, k = 3) 
## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## ℹ The deprecated feature was likely used in the factoextra package.
##   Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
tictoc::toc()
## 22.568 sec elapsed
res10$statistics_gcms

summary —-

tictoc::tic()
s_sum <- chooseGCM::summary_gcms(s, var_names, study_area_parana) 
tictoc::toc()
## 1.019 sec elapsed
s_sum
## $ac
##         min quantile_0.25 median      mean quantile_0.75   max       sd NAs
## bio5   30.0         33.10   35.0  34.85827         36.60  38.9  2.07434   0
## bio13 219.9        281.45  312.5 322.35955        367.15 447.1 49.94167   0
## bio15  21.1         31.35   36.0  35.79873         40.00  52.8  6.70125   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ae
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.5          31.7   33.9  33.77850         35.80  39.0  2.456584   0
## bio13 160.8         205.0  234.9 232.03409        253.85 346.4 33.989464   0
## bio15  13.6          23.0   27.0  27.87256         32.40  49.9  7.274955   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $cc
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   32.6         37.00   40.5  40.15403          43.2  46.2  3.572336   0
## bio13 120.6        162.95  174.8 179.43027         190.9 259.9 22.875207   0
## bio15  25.7         35.80   41.3  41.72546          46.8  64.0  8.123692   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ce
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.3         31.00   33.4  33.13451         35.20  37.6  2.402536   0
## bio13 154.9        188.40  207.8 210.41867        228.45 352.4 30.141739   0
## bio15  22.0         30.05   34.4  35.71697         39.70  61.8  7.875488   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ch
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   30.1         32.80   35.0  34.77143         36.80  38.2  2.234700   0
## bio13 157.6        197.75  227.8 234.44653        265.45 349.1 42.821337   0
## bio15  21.1         29.45   34.1  34.04272         37.60  50.8  6.026132   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $cn
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.6         32.30   34.2  34.04342         35.85  37.8  2.027760   0
## bio13 161.4        201.60  231.4 242.91598        284.05 365.6 50.212614   0
## bio15  21.6         29.05   33.7  33.76478         37.75  50.2  5.928112   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $cr
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.5         32.20   33.9  33.75672         35.40  37.4  1.883030   0
## bio13 153.0        196.95  224.1 228.96860        258.75 324.9 38.515889   0
## bio15  17.8         27.20   32.8  33.03182         37.90  52.0  7.391182   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ec
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.9         30.65   33.4  33.68529         36.50  40.5  3.351218   0
## bio13 150.3        191.25  220.2 223.01669        250.35 346.0 38.295019   0
## bio15  23.3         29.90   32.9  33.70778         37.40  48.7  5.044691   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ev
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.1         30.90   34.0  34.13607          37.1  40.9  3.461977   0
## bio13 145.4        179.20  201.9 208.62999         232.9 360.9 37.832441   0
## bio15  24.0         30.35   33.9  34.69505          37.9  52.0  5.371725   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $fi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.8         31.90   33.9  33.70438          35.5  38.4  2.114759   0
## bio13 159.8        201.85  219.9 225.56775         250.0 337.8 31.601529   0
## bio15  17.7         25.55   29.6  30.53762          34.1  52.2  6.643050   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $gg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.2         30.80   32.9  32.66662          34.5  36.6  2.085486   0
## bio13 142.0        176.30  197.1 196.48373         213.4 318.6 24.400397   0
## bio15  11.2         18.75   22.5  23.91513          27.7  46.7  7.179333   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $gh
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.0          30.7   32.7  32.65842          34.6  36.5  2.202657   0
## bio13 143.7         181.3  208.1 209.30170         237.4 298.2 31.733054   0
## bio15  19.1          25.6   28.1  29.45078          32.8  50.2  5.974342   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $hg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   30.9          34.5   36.7  36.54314          38.7  41.1  2.421853   0
## bio13 167.8         210.9  235.3 244.25516         275.6 379.1 41.680786   0
## bio15  15.1          22.9   26.9  28.00778          31.9  48.7  6.588587   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ic
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.3          30.7   32.5  32.41697         34.00  36.4  1.903841   0
## bio13 137.3         170.3  186.2 193.20354        213.95 298.1 28.884706   0
## bio15  14.2          23.7   27.7  28.84356         33.10  51.6  7.329611   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $`in`
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.4          30.8   32.5  32.48020         34.00  37.0  1.914905   0
## bio13 144.7         184.6  199.9 202.81683        214.10 373.5 25.602512   0
## bio15  13.6          23.5   28.0  28.85955         33.75  53.0  8.055430   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ip
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.5         32.75   35.8  35.52984         38.00  40.7  3.006113   0
## bio13 146.1        181.20  194.6 197.28487        211.15 303.5 21.504443   0
## bio15  15.5         26.30   32.8  33.45799         39.50  61.9  9.749642   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $me
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.3         29.60   31.4  31.34767          32.9  35.3  1.921691   0
## bio13 146.2        178.95  199.9 204.60934         226.6 316.6 30.162977   0
## bio15  15.0         25.90   28.8  29.88161          33.7  51.1  6.903009   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $mi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.4          32.0   33.1  33.08105         34.20  37.0  1.511685   0
## bio13 156.9         190.6  209.1 215.29208        239.75 324.6 30.811766   0
## bio15  16.5          25.9   29.3  29.73678         33.40  45.1  5.746360   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $ml
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.1         30.50   32.3  32.26351         33.95  36.9  1.985585   0
## bio13 141.3        173.20  188.6 190.05262        202.90 339.6 24.682023   0
## bio15  18.0         28.45   33.0  34.31146         39.80  58.6  8.766429   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $mp
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.0         30.30   32.0  32.00636         33.70  36.0  1.957350   0
## bio13 150.3        189.10  208.4 210.16040        228.40 307.4 28.147191   0
## bio15  15.0         25.35   29.4  30.95092         35.95  54.1  8.208039   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $mr
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.9          30.4   32.1  31.95361         33.50  36.2  1.845843   0
## bio13 167.5         210.4  243.4 244.34851        275.65 330.1 38.164065   0
## bio15  17.9          26.3   32.3  32.14031         37.05  50.9  7.377425   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707
## 
## $uk
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   31.0          34.1   36.3  36.28939          38.4  41.5  2.521306   0
## bio13 154.9         198.9  228.4 234.23621         267.7 379.0 44.530524   0
## bio15  17.2          25.7   30.4  30.83777          35.3  49.4  6.706006   0
##       n_cells
## bio5      707
## bio13     707
## bio15     707

correlation —-

tictoc::tic()
s_cor <- chooseGCM::cor_gcms(s, var_names, study_area_parana, method = "pearson") 
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
tictoc::toc()
## 0.988 sec elapsed
s_cor
## $cor_matrix
##           ac        ae        cc        ce        ch        cn        cr
## ac 1.0000000 0.8221623 0.7779686 0.7169188 0.9268902 0.9706512 0.9692144
## ae 0.8221623 1.0000000 0.8690880 0.9059237 0.8964003 0.8660950 0.8842169
## cc 0.7779686 0.8690880 1.0000000 0.8749034 0.8142541 0.7982917 0.8337845
## ce 0.7169188 0.9059237 0.8749034 1.0000000 0.8315531 0.7650128 0.7983690
## ch 0.9268902 0.8964003 0.8142541 0.8315531 1.0000000 0.9491279 0.9630421
## cn 0.9706512 0.8660950 0.7982917 0.7650128 0.9491279 1.0000000 0.9822694
## cr 0.9692144 0.8842169 0.8337845 0.7983690 0.9630421 0.9822694 1.0000000
## ec 0.8349417 0.9136489 0.7905206 0.8093051 0.9017900 0.8806604 0.8778921
## ev 0.8170684 0.9060546 0.8150262 0.8426813 0.9035868 0.8794336 0.8723896
## fi 0.8568288 0.9221515 0.8636071 0.9040518 0.9075295 0.8967866 0.9097130
## gg 0.8361614 0.9446461 0.8706672 0.9267101 0.8968419 0.8727489 0.9079228
## gh 0.9095253 0.8322720 0.7753705 0.7362790 0.8830002 0.9155257 0.9210175
## hg 0.8802669 0.9315131 0.8199187 0.8325131 0.9111676 0.9103634 0.9139112
## ic 0.9055991 0.9191887 0.8768717 0.8760769 0.9475743 0.9284401 0.9549257
## in 0.7283280 0.8899324 0.8719957 0.9011391 0.8107158 0.7694313 0.8103198
## ip 0.6781061 0.8952220 0.9234523 0.9032355 0.7509823 0.7044157 0.7561158
## me 0.8923429 0.9314834 0.8655184 0.8742634 0.9340380 0.9206270 0.9461317
## mi 0.9337817 0.8932794 0.8243319 0.8122928 0.9402338 0.9420059 0.9673063
## ml 0.7751363 0.9281638 0.8754045 0.9372642 0.8478131 0.8124633 0.8520258
## mp 0.7946159 0.9472760 0.8701070 0.9365527 0.8620874 0.8304357 0.8569165
## mr 0.9796740 0.7846757 0.7578737 0.6833181 0.9117286 0.9533497 0.9638900
## uk 0.8667022 0.9426605 0.8387373 0.8451482 0.9186445 0.9030544 0.9120982
##           ec        ev        fi        gg        gh        hg        ic
## ac 0.8349417 0.8170684 0.8568288 0.8361614 0.9095253 0.8802669 0.9055991
## ae 0.9136489 0.9060546 0.9221515 0.9446461 0.8322720 0.9315131 0.9191887
## cc 0.7905206 0.8150262 0.8636071 0.8706672 0.7753705 0.8199187 0.8768717
## ce 0.8093051 0.8426813 0.9040518 0.9267101 0.7362790 0.8325131 0.8760769
## ch 0.9017900 0.9035868 0.9075295 0.8968419 0.8830002 0.9111676 0.9475743
## cn 0.8806604 0.8794336 0.8967866 0.8727489 0.9155257 0.9103634 0.9284401
## cr 0.8778921 0.8723896 0.9097130 0.9079228 0.9210175 0.9139112 0.9549257
## ec 1.0000000 0.9768839 0.8874581 0.8586740 0.8349162 0.9386699 0.8496625
## ev 0.9768839 1.0000000 0.9065067 0.8724732 0.8186457 0.9421069 0.8635192
## fi 0.8874581 0.9065067 1.0000000 0.9519501 0.8771491 0.9532972 0.9296169
## gg 0.8586740 0.8724732 0.9519501 1.0000000 0.8811278 0.9099681 0.9460605
## gh 0.8349162 0.8186457 0.8771491 0.8811278 1.0000000 0.8725502 0.8701500
## hg 0.9386699 0.9421069 0.9532972 0.9099681 0.8725502 1.0000000 0.9064677
## ic 0.8496625 0.8635192 0.9296169 0.9460605 0.8701500 0.9064677 1.0000000
## in 0.7582028 0.7879370 0.8827371 0.9286185 0.7322632 0.8308887 0.8999875
## ip 0.7624028 0.7807836 0.8327634 0.8885051 0.7185161 0.7902233 0.8306863
## me 0.8537084 0.8612248 0.9276555 0.9556698 0.8783703 0.9112265 0.9893202
## mi 0.8620506 0.8606672 0.9137509 0.9210249 0.8792560 0.9173857 0.9658573
## ml 0.8139954 0.8416309 0.9292728 0.9661893 0.8058758 0.8796517 0.9150769
## mp 0.8335828 0.8505111 0.9424856 0.9472100 0.8181699 0.8959109 0.9151773
## mr 0.8059343 0.7899833 0.8413499 0.8243118 0.9300504 0.8537551 0.8889867
## uk 0.9517633 0.9588460 0.9359616 0.9042762 0.8422931 0.9848134 0.9161233
##           in        ip        me        mi        ml        mp        mr
## ac 0.7283280 0.6781061 0.8923429 0.9337817 0.7751363 0.7946159 0.9796740
## ae 0.8899324 0.8952220 0.9314834 0.8932794 0.9281638 0.9472760 0.7846757
## cc 0.8719957 0.9234523 0.8655184 0.8243319 0.8754045 0.8701070 0.7578737
## ce 0.9011391 0.9032355 0.8742634 0.8122928 0.9372642 0.9365527 0.6833181
## ch 0.8107158 0.7509823 0.9340380 0.9402338 0.8478131 0.8620874 0.9117286
## cn 0.7694313 0.7044157 0.9206270 0.9420059 0.8124633 0.8304357 0.9533497
## cr 0.8103198 0.7561158 0.9461317 0.9673063 0.8520258 0.8569165 0.9638900
## ec 0.7582028 0.7624028 0.8537084 0.8620506 0.8139954 0.8335828 0.8059343
## ev 0.7879370 0.7807836 0.8612248 0.8606672 0.8416309 0.8505111 0.7899833
## fi 0.8827371 0.8327634 0.9276555 0.9137509 0.9292728 0.9424856 0.8413499
## gg 0.9286185 0.8885051 0.9556698 0.9210249 0.9661893 0.9472100 0.8243118
## gh 0.7322632 0.7185161 0.8783703 0.8792560 0.8058758 0.8181699 0.9300504
## hg 0.8308887 0.7902233 0.9112265 0.9173857 0.8796517 0.8959109 0.8537551
## ic 0.8999875 0.8306863 0.9893202 0.9658573 0.9150769 0.9151773 0.8889867
## in 1.0000000 0.9024846 0.9086404 0.8680603 0.9544819 0.9068467 0.7100872
## ip 0.9024846 1.0000000 0.8357375 0.7723642 0.9179682 0.8997399 0.6640410
## me 0.9086404 0.8357375 1.0000000 0.9650137 0.9274084 0.9222020 0.8709459
## mi 0.8680603 0.7723642 0.9650137 1.0000000 0.8871580 0.8759934 0.9204307
## ml 0.9544819 0.9179682 0.9274084 0.8871580 1.0000000 0.9692117 0.7607951
## mp 0.9068467 0.8997399 0.9222020 0.8759934 0.9692117 1.0000000 0.7700381
## mr 0.7100872 0.6640410 0.8709459 0.9204307 0.7607951 0.7700381 1.0000000
## uk 0.8347216 0.8076055 0.9201462 0.9171602 0.8817252 0.8962239 0.8356316
##           uk
## ac 0.8667022
## ae 0.9426605
## cc 0.8387373
## ce 0.8451482
## ch 0.9186445
## cn 0.9030544
## cr 0.9120982
## ec 0.9517633
## ev 0.9588460
## fi 0.9359616
## gg 0.9042762
## gh 0.8422931
## hg 0.9848134
## ic 0.9161233
## in 0.8347216
## ip 0.8076055
## me 0.9201462
## mi 0.9171602
## ml 0.8817252
## mp 0.8962239
## mr 0.8356316
## uk 1.0000000
## 
## $cor_plot

distance —-

tictoc::tic()
s_dist <- chooseGCM::dist_gcms(s, var_names, study_area_parana, method = "euclidean")
tictoc::toc()
## 0.933 sec elapsed
s_dist
## $distances
##           ac        ae        cc        ce        ch        cn        cr
## ae 27.446681                                                            
## cc 30.667980 23.548745                                                  
## ce 34.628484 19.962645 23.019761                                        
## ch 17.598104 20.948707 28.050305 26.712186                              
## cn 11.149951 23.816414 29.230741 31.550051 14.679723                    
## cr 11.419615 22.146265 26.534672 29.225141 12.512146  8.666425          
## ec 26.442146 19.125466 29.788501 28.421533 20.396504 22.483828 22.743114
## ev 27.836995 19.948749 27.991945 25.814760 20.209062 22.599099 23.249899
## fi 24.626673 18.159462 24.036645 20.160271 19.791534 20.909617 19.556480
## gg 26.344267 15.312714 23.406276 17.619761 20.904009 23.217139 19.749404
## gh 19.576790 26.655126 30.846886 33.423379 22.262322 18.916481 18.291254
## hg 22.520868 17.032625 27.619274 26.635960 19.398296 19.485910 19.096395
## ic 19.997050 18.501802 22.837944 22.911527 14.902189 17.410565 13.817914
## in 33.923480 21.592735 23.285747 20.463989 28.316214 31.252023 28.345818
## ip 36.926176 21.067497 18.007113 20.245846 32.478287 35.384956 32.141774
## me 21.354983 17.036328 23.867637 23.078569 16.715711 18.336408 15.105834
## mi 16.748160 21.261901 27.278746 28.198007 15.911306 15.673647 11.768199
## ml 30.862963 17.444144 22.973607 16.301801 25.390231 28.185201 25.036346
## mp 29.495883 14.944524 23.456912 16.393990 24.170187 26.800642 24.619135
## mr  9.279065 30.201222 32.025727 36.626010 19.336955 14.057400 12.367787
## uk 23.762350 15.584933 26.136351 25.611562 18.563994 20.264791 19.296427
##           ec        ev        fi        gg        gh        hg        ic
## ae                                                                      
## cc                                                                      
## ce                                                                      
## ch                                                                      
## cn                                                                      
## cr                                                                      
## ec                                                                      
## ev  9.895444                                                            
## fi 21.834094 19.900690                                                  
## gg 24.467468 23.242281 14.266717                                        
## gh 26.444187 27.716722 22.812199 22.439752                              
## hg 16.118141 15.659990 14.065311 19.528833 23.235264                    
## ic 25.235483 24.044388 17.266817 15.115804 23.453032 19.904844          
## in 32.003952 29.971632 22.287339 17.388854 33.676893 26.764818 20.582831
## ip 31.724781 30.472949 26.616049 21.732288 34.530650 29.809629 26.780826
## me 24.893601 24.245652 17.505754 13.703384 22.698531 19.391866  6.726042
## mi 24.173410 24.294316 19.114162 18.290392 22.615738 18.707058 12.026155
## ml 28.069834 25.900799 17.308966 11.967548 28.675952 22.578652 18.966658
## mp 26.550767 25.164163 15.608686 14.953883 27.753063 20.998126 18.955446
## mr 28.671630 29.826681 25.923768 27.280310 17.213552 24.889621 21.685301
## uk 14.294423 13.203345 16.470173 20.136688 25.846591  8.020631 18.849452
##           in        ip        me        mi        ml        mp        mr
## ae                                                                      
## cc                                                                      
## ce                                                                      
## ch                                                                      
## cn                                                                      
## cr                                                                      
## ec                                                                      
## ev                                                                      
## fi                                                                      
## gg                                                                      
## gh                                                                      
## hg                                                                      
## ic                                                                      
## in                                                                      
## ip 20.324249                                                            
## me 19.672293 26.378324                                                  
## mi 23.640991 31.052624 12.173819                                        
## ml 13.885772 18.640998 17.535623 21.863181                              
## mp 19.864473 20.608296 18.153574 22.919246 11.420123                    
## mr 35.043838 37.724291 23.381047 18.359078 31.831936 31.210876          
## uk 26.459766 28.547906 18.391860 18.732579 22.383299 20.966532 26.386822
## 
## $heatmap

k-means —-

tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3, method = "euclidean") 
## $suggested_gcms
##    1    2    3 
## "ml" "uk" "cr" 
## 
## $kmeans_plot

tictoc::toc()
## 1.886 sec elapsed
tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3)
## $suggested_gcms
## [1] "mp" "gh" "ip"
## 
## $kmeans_plot

tictoc::toc()
## 1.65 sec elapsed

hierarchical clustering —-

tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "me" "fi" "hg"
## 
## $dend_plot

tictoc::toc()
## 1.28 sec elapsed
tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3, n = 1000) 
## $suggested_gcms
## [1] "me" "hg" "fi"
## 
## $dend_plot

tictoc::toc()
## 1.273 sec elapsed

Closestdist algorithm —-

tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "gh" "me" "uk"
## 
## $best_mean_diff
## [1] 2.547513e-05
## 
## $global_mean
## [1] 22.31235
tictoc::toc()
## 1.015 sec elapsed
tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana) 
## $suggested_gcms
## [1] "gh" "me" "uk"
## 
## $best_mean_diff
## [1] 2.547513e-05
## 
## $global_mean
## [1] 22.31235
tictoc::toc()
## 1.152 sec elapsed

number of clusters —-

tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "wss", n = 1000) 

tictoc::toc()
## 1.371 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "silhouette", n = 1000) 

tictoc::toc()
## 1.24 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "gap_stat", n = 1000) 
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations

tictoc::toc()
## 43.193 sec elapsed

monte carlo permutations —-

tictoc::tic()
chooseGCM::montecarlo_gcms(s, var_names, study_area_parana, perm = 10000, method = "euclidean") 
## $montecarlo_plot

## 
## $suggested_gcms
## $suggested_gcms$k2
## [1] "fi" "in"
## 
## $suggested_gcms$k3
## [1] "gh" "me" "uk"
## 
## $suggested_gcms$k4
## [1] "ce" "mi" "fi" "in"
## 
## $suggested_gcms$k5
## [1] "ce" "ev" "gg" "in" "hg"
## 
## $suggested_gcms$k6
## [1] "ec" "fi" "ml" "cr" "ac" "ev"
## 
## $suggested_gcms$k7
## [1] "gg" "ic" "ec" "ce" "in" "ch" "ev"
## 
## $suggested_gcms$k8
## [1] "ae" "ch" "ce" "ev" "mi" "cn" "ac" "mr"
## 
## $suggested_gcms$k9
## [1] "ce" "mi" "fi" "in" "cr" "cn" "mr" "uk" "ml"
## 
## $suggested_gcms$k10
##  [1] "ce" "ch" "fi" "cn" "ev" "ml" "ec" "mi" "ac" "mp"
## 
## $suggested_gcms$k11
##  [1] "ae" "ic" "ip" "cr" "in" "ch" "hg" "ev" "cn" "ec" "ml"
## 
## $suggested_gcms$k12
##  [1] "ce" "me" "uk" "in" "ch" "ev" "ec" "cn" "ml" "mp" "cr" "ac"
## 
## $suggested_gcms$k13
##  [1] "fi" "me" "mr" "ml" "in" "uk" "cn" "ac" "mp" "gh" "ae" "ch" "ev"
## 
## $suggested_gcms$k14
##  [1] "ac" "ae" "mr" "uk" "ev" "gg" "mp" "ml" "ec" "cn" "gh" "mi" "ce" "hg"
## 
## $suggested_gcms$k15
##  [1] "ic" "me" "ip" "cn" "in" "uk" "ch" "ac" "mp" "ml" "ce" "hg" "ev" "cr" "ec"
## 
## $suggested_gcms$k16
##  [1] "cr" "ec" "gg" "gh" "mr" "ev" "ae" "mp" "ac" "fi" "ml" "ce" "ic" "cn" "in"
## [16] "ch"
## 
## $suggested_gcms$k17
##  [1] "ae" "ec" "ce" "ic" "cr" "ml" "in" "ev" "cn" "mp" "cc" "uk" "ch" "ac" "mi"
## [16] "mr" "fi"
## 
## $suggested_gcms$k18
##  [1] "gh" "ml" "mp" "ce" "in" "ip" "ic" "mi" "cr" "ae" "hg" "cn" "ch" "ac" "uk"
## [16] "ev" "fi" "ec"
## 
## $suggested_gcms$k19
##  [1] "ac" "ev" "mr" "ic" "ec" "fi" "gh" "gg" "ae" "mp" "ml" "cn" "ce" "ch" "uk"
## [16] "in" "mi" "cc" "cr"
## 
## $suggested_gcms$k20
##  [1] "cc" "fi" "ce" "in" "ip" "me" "mi" "uk" "hg" "cr" "ch" "ev" "cn" "mp" "ec"
## [16] "ml" "ac" "ae" "gh" "ic"
## 
## $suggested_gcms$k21
##  [1] "ae" "cc" "ce" "uk" "ip" "in" "hg" "ic" "mi" "cr" "ch" "ev" "cn" "mp" "ec"
## [16] "ml" "ac" "fi" "mr" "me" "gh"
tictoc::toc()
## 18.253 sec elapsed

environment —-

tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = res10$suggested_gcms$k3) 

tictoc::toc()
## 1.819 sec elapsed
tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = "sum")

tictoc::toc()
## 1.879 sec elapsed

5 min ——————————————————————

import —-

tictoc::tic()
s <- chooseGCM::import_gcms(path = "~/storage/WC_data/WC_data_all_gcms_5") 
tictoc::toc()
## 0.516 sec elapsed
s
## $ac_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ac_ssp585_5_2090.tif 
## names       :  bio1, bio2,   bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.9, -2.6, -130.4,    7.5, -24.8, -65.4, ... 
## max values  :  38.3, 22.0,   94.3, 2252.1,  56.5,  30.6, ... 
## 
## $ae_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ae_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.5, -1.0, -10.5,   11.7, -25.2, -67.5, ... 
## max values  :  37.0, 21.9,  94.4, 2237.6,  55.7,  29.1, ... 
## 
## $cc_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cc_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -45.1,   -2, -33.0,   13.4, -23.8, -65.0, ... 
## max values  :  39.8,   22,  95.1, 2231.1,  58.6,  32.4, ... 
## 
## $ce_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ce_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.3, -3.0, -38.2,   12.4, -25.4, -67.5, ... 
## max values  :  37.3, 22.8,  95.4, 2289.3,  55.6,  29.3, ... 
## 
## $ch_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ch_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.9, -2.6, -54.6,   13.6, -23.3, -67.6, ... 
## max values  :  37.8, 22.6,  94.3, 2258.2,  58.6,  29.7, ... 
## 
## $cn_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cn_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -3.2, -64.1,   13.7, -23.8, -66.6, ... 
## max values  :  37.8, 22.9,  95.4, 2168.6,  56.9,  29.7, ... 
## 
## $cr_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cr_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.7, -39.6,    8.9, -24.3, -67.8, ... 
## max values  :  37.4, 22.9,  95.7, 2188.3,  56.0,  29.6, ... 
## 
## $ec_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ec_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.6, -2.7, -26.6,   11.1, -26.0, -68.5, ... 
## max values  :  38.0, 21.9,  95.9, 2365.1,  55.8,  30.2, ... 
## 
## $ev_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ev_ssp585_5_2090.tif 
## names       : bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  :  -49, -2.6, -16.1,   10.5, -26.2, -67.1, ... 
## max values  :   38, 21.8,  96.3, 2361.9,  55.6,  29.9, ... 
## 
## $fi_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : fi_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.4, -2.2, -30.0,   10.5, -24.6, -66.7, ... 
## max values  :  37.0, 21.9,  94.3, 2191.5,  55.0,  29.0, ... 
## 
## $gg_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gg_ssp585_5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.1, -0.4, -3.2,    7.8, -25.3, -66.2, ... 
## max values  :  36.9, 22.0, 96.5, 2274.6,  55.0,  28.7, ... 
## 
## $gh_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gh_ssp585_5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -0.3, -2.4,    6.6, -25.6, -65.9, ... 
## max values  :  37.2, 22.8, 95.8, 2261.7,  55.6,  28.9, ... 
## 
## $hg_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : hg_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -2.7, -80.7,   10.1, -24.9, -66.6, ... 
## max values  :  38.6, 21.4,  95.3, 2296.9,  57.0,  31.6, ... 
## 
## $ic_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ic_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.0, -2.5, -21.7,    9.0, -25.6, -69.1, ... 
## max values  :  35.4, 22.6,  95.2, 2328.5,  52.7,  27.8, ... 
## 
## $in_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : in_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.5, -1.8, -23.3,    6.8, -25.4, -67.2, ... 
## max values  :  35.7, 23.2,  95.0, 2345.6,  53.5,  28.7, ... 
## 
## $ip_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ip_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.3, -20.3,    8.0, -25.8, -65.5, ... 
## max values  :  38.0, 21.7,  95.1, 2195.9,  56.5,  29.8, ... 
## 
## $me_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : me_ssp585_5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.7, -1.0, -8.2,    9.2, -28.1, -69.2, ... 
## max values  :  36.4, 23.1, 94.6, 2242.0,  54.7,  28.2, ... 
## 
## $mi_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mi_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.4, -1.9, -11.0,   10.6, -26.9, -68.4, ... 
## max values  :  36.4, 23.1,  95.5, 2258.9,  55.2,  27.9, ... 
## 
## $ml_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ml_ssp585_5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.3, -0.7, -4.6,   10.5, -26.5, -68.8, ... 
## max values  :  35.4, 21.7, 95.7, 2254.0,  54.3,  28.1, ... 
## 
## $mp_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mp_ssp585_5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.8, -0.5, -3.2,   13.7, -26.3, -67.1, ... 
## max values  :  35.2, 21.6, 96.1, 2286.9,  53.7,  28.1, ... 
## 
## $mr_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mr_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.7, -1.6, -20.6,    8.3, -24.6, -68.3, ... 
## max values  :  36.6, 22.6,  94.6, 2157.6,  55.2,  28.5, ... 
## 
## $uk_ssp585_5_2090
## class       : SpatRaster 
## dimensions  : 2160, 4320, 19  (nrow, ncol, nlyr)
## resolution  : 0.08333333, 0.08333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : uk_ssp585_5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.6, -3.5, -71.4,    8.8, -24.1, -66.6, ... 
## max values  :  38.9, 21.5,  95.1, 2326.5,  57.5,  31.2, ...
names(s)
##  [1] "ac_ssp585_5_2090" "ae_ssp585_5_2090" "cc_ssp585_5_2090" "ce_ssp585_5_2090"
##  [5] "ch_ssp585_5_2090" "cn_ssp585_5_2090" "cr_ssp585_5_2090" "ec_ssp585_5_2090"
##  [9] "ev_ssp585_5_2090" "fi_ssp585_5_2090" "gg_ssp585_5_2090" "gh_ssp585_5_2090"
## [13] "hg_ssp585_5_2090" "ic_ssp585_5_2090" "in_ssp585_5_2090" "ip_ssp585_5_2090"
## [17] "me_ssp585_5_2090" "mi_ssp585_5_2090" "ml_ssp585_5_2090" "mp_ssp585_5_2090"
## [21] "mr_ssp585_5_2090" "uk_ssp585_5_2090"
names(s) <- gsub("_ssp585_5_2090", "", names(s))
names(s)
##  [1] "ac" "ae" "cc" "ce" "ch" "cn" "cr" "ec" "ev" "fi" "gg" "gh" "hg" "ic" "in"
## [16] "ip" "me" "mi" "ml" "mp" "mr" "uk"

variable names and study area —-

var_names <- c("bio5", "bio13", "bio15")

study_area_parana <- geodata::gadm(country = "Brazil", path = "input_data/") %>% 
  sf::st_as_sf() %>% 
  dplyr::filter(NAME_1 == "Paraná")
study_area_parana
## Simple feature collection with 1 feature and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -54.61602 ymin: -26.71712 xmax: -48.02354 ymax: -22.5163
## Geodetic CRS:  WGS 84
##      GID_1 GID_0 COUNTRY NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1
## 1 BRA.16_1   BRA  Brazil Paraná      <NA>      <NA> Estado     State <NA>
##   HASC_1 ISO_1                       geometry
## 1  BR.PR  <NA> MULTIPOLYGON (((-52.52423 -...
plot(study_area_parana$geometry)

compare —-

tictoc::tic()
res5 <- chooseGCM::compare_gcms(s, var_names, study_area_parana, k = 3) 
tictoc::toc()
## 26.812 sec elapsed
res5$statistics_gcms

summary —-

tictoc::tic()
s_sum <- chooseGCM::summary_gcms(s, var_names, study_area_parana)
tictoc::toc()
## 2.083 sec elapsed
s_sum
## $ac
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.4          33.1     35  34.82698          36.5  39.0  2.073704   0
## bio13 219.0         282.5    314 323.65810         368.5 451.5 50.172973   0
## bio15  20.6          31.5     36  35.80107          40.0  53.5  6.485186   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ae
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.9          31.7   33.8  33.73835          35.7  39.0  2.431370   0
## bio13 160.8         205.3  235.5 232.75633         254.8 374.8 35.017405   0
## bio15  13.8          23.1   27.0  27.81838          32.3  50.0  7.036772   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $cc
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   30.9          36.9   40.4  40.11812          43.2  46.3  3.545503   0
## bio13 115.5         163.0  175.3 180.03632         192.0 275.5 23.445537   0
## bio15  24.8          36.0   41.5  41.70971          46.9  64.1  7.909567   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ce
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.7          31.0   33.3  33.10181          35.1  37.7  2.388513   0
## bio13 154.3         188.3  207.5 210.95426         228.0 363.5 31.567165   0
## bio15  21.5          30.3   34.5  35.66231          39.7  62.1  7.570596   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ch
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.4          32.8     35  34.74396          36.7  38.3  2.238552   0
## bio13 157.5         198.0    228 234.88870         265.0 362.0 43.158342   0
## bio15  20.9          29.5     34  33.99712          37.5  51.2  5.821909   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $cn
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.1          32.3   34.2  34.01347          35.8  37.9  2.028597   0
## bio13 159.5         201.8  232.0 243.74581         284.3 369.0 50.317043   0
## bio15  21.4          29.1   33.6  33.69815          37.6  50.8  5.747389   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $cr
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.0          32.1   33.9  33.72842          35.4  37.5  1.885993   0
## bio13 150.0         197.5  225.0 229.80568         259.0 348.0 38.730246   0
## bio15  17.3          27.3   32.9  32.99155          37.9  52.6  7.171921   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ec
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.5          30.6   33.3  33.64688          36.5  40.5  3.324038   0
## bio13 150.0         190.5  221.3 223.40609         251.0 360.3 38.600105   0
## bio15  22.8          30.0   32.9  33.63994          37.2  48.8  4.860260   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ev
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.7          31.0   34.0  34.09690          37.0  41.0  3.432284   0
## bio13 143.3         179.5  202.3 208.94352         232.0 393.8 38.275202   0
## bio15  24.2          30.4   34.0  34.58143          37.9  52.3  5.166289   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $fi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.1          31.9   33.9  33.67250          35.4  38.5  2.108177   0
## bio13 153.3         201.0  220.3 226.20609         250.3 369.3 32.353017   0
## bio15  17.8          25.7   29.6  30.48616          33.9  52.4  6.421426   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $gg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.6          30.9   32.8  32.63920          34.4  36.7  2.085860   0
## bio13 136.8         176.5  197.3 197.16180         213.5 348.3 25.485761   0
## bio15  11.3          18.8   22.5  23.86737          27.8  47.1  6.944005   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $gh
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.5          30.7   32.8  32.62798          34.5  36.6  2.199624   0
## bio13 141.3         181.3  208.8 209.91528         237.8 306.3 31.802287   0
## bio15  19.1          25.6   28.1  29.40742          32.7  50.9  5.750448   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $hg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.2          34.5   36.7  36.50310          38.6  41.2  2.397355   0
## bio13 167.0         211.3  237.3 245.02920         277.5 395.3 42.286091   0
## bio15  14.8          23.0   27.0  27.94625          31.8  49.0  6.366479   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ic
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.0          30.8   32.5  32.38597          33.9  36.5  1.903756   0
## bio13 134.5         170.8  187.0 193.93108         214.0 324.5 29.788108   0
## bio15  14.3          24.0   27.8  28.81639          33.2  52.2  7.106840   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $`in`
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.4          30.8   32.5  32.44921          34.0  37.1  1.916697   0
## bio13 140.3         184.8  199.8 203.58498         215.0 402.8 27.624952   0
## bio15  13.8          23.5   27.9  28.82750          33.3  53.3  7.870385   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ip
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.0          32.8   35.8  35.49679          38.0  40.8  2.984111   0
## bio13 139.8         180.8  195.0 197.76604         211.5 324.8 22.457526   0
## bio15  15.2          26.4   32.7  33.38376          39.5  62.3  9.483548   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $me
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.3          29.6   31.4  31.31436          32.9  35.4  1.926233   0
## bio13 143.8         179.3  201.0 205.34932         227.3 348.3 30.846710   0
## bio15  15.2          25.9   28.8  29.84736          33.7  51.5  6.682520   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $mi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.9          32.0   33.1  33.05489          34.2  37.0  1.514363   0
## bio13 154.0         191.5  210.0 216.29487         240.3 358.8 31.408972   0
## bio15  16.6          26.0   29.2  29.70520          33.3  45.5  5.575833   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $ml
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.9          30.5   32.3  32.22743          33.8  37.0  1.981039   0
## bio13 134.5         173.0  188.8 190.58594         203.3 371.5 25.946013   0
## bio15  17.8          28.5   33.0  34.22503          39.7  58.8  8.533996   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $mp
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.7          30.3   32.0  31.97350          33.6  36.2  1.952722   0
## bio13 146.5         189.5  207.8 210.64880         229.5 320.0 29.001875   0
## bio15  14.8          25.4   29.4  30.88424          35.8  54.2  7.965020   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $mr
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.4          30.4   32.1  31.92614          33.5  36.3  1.844875   0
## bio13 165.8         211.5  244.0 245.28224         276.0 337.8 38.182205   0
## bio15  18.1          26.6   32.2  32.11473          37.1  51.0  7.143655   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709
## 
## $uk
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.3          34.1   36.3  36.24732          38.3  41.6  2.496220   0
## bio13 154.3         199.8  229.5 235.09598         267.3 414.3 45.107537   0
## bio15  16.9          25.9   30.4  30.77818          35.2  49.9  6.522233   0
##       n_cells
## bio5     2709
## bio13    2709
## bio15    2709

correlation —-

tictoc::tic()
s_cor <- chooseGCM::cor_gcms(s, var_names, study_area_parana, method = "pearson") 
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
tictoc::toc()
## 1.921 sec elapsed
s_cor
## $cor_matrix
##           ac        ae        cc        ce        ch        cn        cr
## ac 1.0000000 0.8173858 0.7711748 0.7179765 0.9241295 0.9705710 0.9671179
## ae 0.8173858 1.0000000 0.8715875 0.9123062 0.8969533 0.8632907 0.8854278
## cc 0.7711748 0.8715875 1.0000000 0.8804813 0.8142440 0.7950763 0.8331667
## ce 0.7179765 0.9123062 0.8804813 1.0000000 0.8394004 0.7692657 0.8059461
## ch 0.9241295 0.8969533 0.8142440 0.8394004 1.0000000 0.9463690 0.9619148
## cn 0.9705710 0.8632907 0.7950763 0.7692657 0.9463690 1.0000000 0.9815911
## cr 0.9671179 0.8854278 0.8331667 0.8059461 0.9619148 0.9815911 1.0000000
## ec 0.8319650 0.9146100 0.7929487 0.8158945 0.9002500 0.8742605 0.8760772
## ev 0.8138890 0.9081171 0.8187434 0.8501454 0.9021403 0.8734238 0.8711197
## fi 0.8529429 0.9237787 0.8635436 0.9092070 0.9082206 0.8946379 0.9101398
## gg 0.8289659 0.9471959 0.8708400 0.9313204 0.8970865 0.8699171 0.9073812
## gh 0.9059004 0.8288497 0.7668125 0.7372022 0.8796565 0.9122240 0.9172521
## hg 0.8757270 0.9298170 0.8175125 0.8363702 0.9099776 0.9057336 0.9124010
## ic 0.8966414 0.9214896 0.8759833 0.8861039 0.9452912 0.9242818 0.9525070
## in 0.7192020 0.8914175 0.8699903 0.9068409 0.8113905 0.7665869 0.8114384
## ip 0.6745655 0.8998360 0.9271555 0.9070398 0.7551244 0.7050712 0.7604993
## me 0.8846945 0.9324686 0.8638381 0.8813768 0.9322724 0.9174094 0.9447358
## mi 0.9270438 0.8949387 0.8222088 0.8221603 0.9392844 0.9384781 0.9661098
## ml 0.7700158 0.9308827 0.8770235 0.9393946 0.8502091 0.8108059 0.8543918
## mp 0.7910133 0.9489404 0.8722258 0.9377844 0.8641827 0.8295766 0.8592487
## mr 0.9792693 0.7832829 0.7529892 0.6889213 0.9098066 0.9533102 0.9627692
## uk 0.8602460 0.9417723 0.8376696 0.8505000 0.9168584 0.8967935 0.9098599
##           ec        ev        fi        gg        gh        hg        ic
## ac 0.8319650 0.8138890 0.8529429 0.8289659 0.9059004 0.8757270 0.8966414
## ae 0.9146100 0.9081171 0.9237787 0.9471959 0.8288497 0.9298170 0.9214896
## cc 0.7929487 0.8187434 0.8635436 0.8708400 0.7668125 0.8175125 0.8759833
## ce 0.8158945 0.8501454 0.9092070 0.9313204 0.7372022 0.8363702 0.8861039
## ch 0.9002500 0.9021403 0.9082206 0.8970865 0.8796565 0.9099776 0.9452912
## cn 0.8742605 0.8734238 0.8946379 0.8699171 0.9122240 0.9057336 0.9242818
## cr 0.8760772 0.8711197 0.9101398 0.9073812 0.9172521 0.9124010 0.9525070
## ec 1.0000000 0.9761416 0.8890413 0.8602106 0.8302732 0.9400636 0.8485564
## ev 0.9761416 1.0000000 0.9091332 0.8762555 0.8129800 0.9439963 0.8648921
## fi 0.8890413 0.9091332 1.0000000 0.9527748 0.8708108 0.9527199 0.9306857
## gg 0.8602106 0.8762555 0.9527748 1.0000000 0.8751100 0.9099832 0.9484557
## gh 0.8302732 0.8129800 0.8708108 0.8751100 1.0000000 0.8663494 0.8613727
## hg 0.9400636 0.9439963 0.9527199 0.9099832 0.8663494 1.0000000 0.9043321
## ic 0.8485564 0.8648921 0.9306857 0.9484557 0.8613727 0.9043321 1.0000000
## in 0.7613339 0.7940403 0.8844752 0.9324937 0.7258098 0.8302495 0.9072868
## ip 0.7691076 0.7883295 0.8358590 0.8920540 0.7158850 0.7916918 0.8373456
## me 0.8522000 0.8614007 0.9274862 0.9570192 0.8722983 0.9080845 0.9890249
## mi 0.8637353 0.8636340 0.9153467 0.9229647 0.8720729 0.9174975 0.9649228
## ml 0.8159719 0.8447415 0.9289325 0.9676883 0.8013216 0.8779044 0.9218777
## mp 0.8339581 0.8513427 0.9419276 0.9479838 0.8145484 0.8933668 0.9196561
## mr 0.8036785 0.7875380 0.8389952 0.8205802 0.9276598 0.8504369 0.8829857
## uk 0.9520154 0.9602204 0.9360604 0.9058653 0.8348261 0.9846209 0.9150244
##           in        ip        me        mi        ml        mp        mr
## ac 0.7192020 0.6745655 0.8846945 0.9270438 0.7700158 0.7910133 0.9792693
## ae 0.8914175 0.8998360 0.9324686 0.8949387 0.9308827 0.9489404 0.7832829
## cc 0.8699903 0.9271555 0.8638381 0.8222088 0.8770235 0.8722258 0.7529892
## ce 0.9068409 0.9070398 0.8813768 0.8221603 0.9393946 0.9377844 0.6889213
## ch 0.8113905 0.7551244 0.9322724 0.9392844 0.8502091 0.8641827 0.9098066
## cn 0.7665869 0.7050712 0.9174094 0.9384781 0.8108059 0.8295766 0.9533102
## cr 0.8114384 0.7604993 0.9447358 0.9661098 0.8543918 0.8592487 0.9627692
## ec 0.7613339 0.7691076 0.8522000 0.8637353 0.8159719 0.8339581 0.8036785
## ev 0.7940403 0.7883295 0.8614007 0.8636340 0.8447415 0.8513427 0.7875380
## fi 0.8844752 0.8358590 0.9274862 0.9153467 0.9289325 0.9419276 0.8389952
## gg 0.9324937 0.8920540 0.9570192 0.9229647 0.9676883 0.9479838 0.8205802
## gh 0.7258098 0.7158850 0.8722983 0.8720729 0.8013216 0.8145484 0.9276598
## hg 0.8302495 0.7916918 0.9080845 0.9174975 0.8779044 0.8933668 0.8504369
## ic 0.9072868 0.8373456 0.9890249 0.9649228 0.9218777 0.9196561 0.8829857
## in 1.0000000 0.9030595 0.9123143 0.8713934 0.9581781 0.9093938 0.7073798
## ip 0.9030595 1.0000000 0.8411633 0.7770806 0.9201753 0.9019922 0.6638222
## me 0.9123143 0.8411633 1.0000000 0.9641333 0.9316329 0.9252043 0.8669400
## mi 0.8713934 0.7770806 0.9641333 1.0000000 0.8918453 0.8791435 0.9158909
## ml 0.9581781 0.9201753 0.9316329 0.8918453 1.0000000 0.9687037 0.7604284
## mp 0.9093938 0.9019922 0.9252043 0.8791435 0.9687037 1.0000000 0.7698315
## mr 0.7073798 0.6638222 0.8669400 0.9158909 0.7604284 0.7698315 1.0000000
## uk 0.8369389 0.8115851 0.9179043 0.9173505 0.8827355 0.8953015 0.8309562
##           uk
## ac 0.8602460
## ae 0.9417723
## cc 0.8376696
## ce 0.8505000
## ch 0.9168584
## cn 0.8967935
## cr 0.9098599
## ec 0.9520154
## ev 0.9602204
## fi 0.9360604
## gg 0.9058653
## gh 0.8348261
## hg 0.9846209
## ic 0.9150244
## in 0.8369389
## ip 0.8115851
## me 0.9179043
## mi 0.9173505
## ml 0.8827355
## mp 0.8953015
## mr 0.8309562
## uk 1.0000000
## 
## $cor_plot

distance —-

tictoc::tic()
s_dist <- chooseGCM::dist_gcms(s, var_names, study_area_parana, method = "euclidean") 
tictoc::toc()
## 1.97 sec elapsed
s_dist
## $distances
##          ac       ae       cc       ce       ch       cn       cr       ec
## ae 54.47124                                                               
## cc 60.97502 45.67764                                                      
## ce 67.69282 37.74716 44.06745                                             
## ch 35.11046 40.91825 54.93781 51.08251                                    
## cn 21.86691 47.13017 57.70270 61.22885 29.51943                           
## cr 23.11423 43.14590 52.06445 56.15147 24.87585 17.29473                  
## ec 52.25162 37.24804 58.00146 54.69320 40.25839 45.19972 44.87201         
## ev 54.99029 38.63825 54.26838 49.34408 39.87511 45.34986 45.76077 19.68887
## fi 48.88133 35.19153 47.08655 38.40838 38.61647 41.37541 38.21058 42.46006
## gg 52.71586 29.29097 45.81040 33.40518 40.89179 45.97376 38.79266 47.65815
## gh 39.10154 52.73376 61.55347 65.34477 44.21923 37.76485 36.66726 52.51401
## hg 44.93537 33.76882 54.45234 51.56216 38.24506 39.13617 37.72676 31.20651
## ic 40.98013 35.71607 44.88900 43.01841 29.81456 35.07519 27.77889 49.60500
## in 67.54558 42.00297 45.96083 38.90564 55.35817 61.58325 55.35114 62.27236
## ip 72.71630 40.34185 34.40317 38.86408 63.07724 69.22429 62.38115 61.24981
## me 43.28376 33.12478 47.03572 43.90204 33.17286 36.63239 29.96553 49.00464
## mi 34.42954 41.31628 53.74710 53.75443 31.40872 31.61658 23.46588 47.05347
## ml 61.12924 33.51147 44.70036 31.38019 49.33358 55.44389 48.63993 54.68171
## mp 58.27191 28.80307 45.56397 31.79431 46.97615 52.62167 47.82183 51.94082
## mr 18.35301 59.33987 63.35165 71.09435 38.28136 27.54298 24.59523 56.47860
## uk 47.65210 30.75847 51.35703 49.28565 36.75439 40.94996 38.27005 27.92228
##          ev       fi       gg       gh       hg       ic       in       ip
## ae                                                                        
## cc                                                                        
## ce                                                                        
## ch                                                                        
## cn                                                                        
## cr                                                                        
## ec                                                                        
## ev                                                                        
## fi 38.42399                                                               
## gg 44.83973 27.70046                                                      
## gh 55.12442 45.81558 45.04678                                             
## hg 30.16535 27.71655 38.24386 46.59994                                    
## ic 46.85332 33.55918 28.93946 47.45962 39.42604                           
## in 57.84837 43.32489 33.11860 66.74611 52.51767 38.81241                  
## ip 58.64488 51.64265 41.87967 67.94336 58.17724 51.40825 39.68740         
## me 47.45484 34.32497 26.42635 45.55104 38.64510 13.35376 37.74542 50.80136
## mi 47.07096 37.08701 35.37894 45.59122 36.61285 23.87329 45.71214 60.18300
## ml 50.22589 33.98095 22.91290 56.81660 44.53997 35.62767 26.06765 36.01378
## mp 49.14655 30.71743 29.07163 54.89279 41.62423 36.13069 38.36887 39.90527
## mr 58.75442 51.14690 53.99271 34.28386 49.29606 43.60331 68.95284 73.90681
## uk 25.42320 32.23183 39.10882 51.80487 15.80756 37.15754 51.47248 55.32960
##          me       mi       ml       mp       mr
## ae                                             
## cc                                             
## ce                                             
## ch                                             
## cn                                             
## cr                                             
## ec                                             
## ev                                             
## fi                                             
## gg                                             
## gh                                             
## hg                                             
## ic                                             
## in                                             
## ip                                             
## me                                             
## mi 24.14047                                    
## ml 33.32909 41.92014                           
## mp 34.86088 44.31340 22.55001                  
## mr 46.49687 36.96763 62.39038 61.15372         
## uk 36.52247 36.64545 43.64989 41.24489 52.40824
## 
## $heatmap

k-means —-

tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3, method = "euclidean") 
## $suggested_gcms
##    1    2    3 
## "uk" "ml" "cr" 
## 
## $kmeans_plot

tictoc::toc()
## 3.014 sec elapsed
tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "gg" "in" "ce"
## 
## $kmeans_plot

tictoc::toc()
## 2.718 sec elapsed

hierarchical clustering —-

tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "cr" "gg" "uk"
## 
## $dend_plot

tictoc::toc()
## 2.409 sec elapsed
tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3, n = 1000) 
## $suggested_gcms
## [1] "cr" "gg" "uk"
## 
## $dend_plot

tictoc::toc()
## 2.396 sec elapsed

Closestdist algorithm —-

tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "ac" "ae" "ic"
## 
## $best_mean_diff
## [1] 0.004153943
## 
## $global_mean
## [1] 43.71833
tictoc::toc()
## 2.187 sec elapsed
tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana) 
## $suggested_gcms
## [1] "cc" "ch" "cr" "hg"
## 
## $best_mean_diff
## [1] 0.00128081
## 
## $global_mean
## [1] 43.71833
tictoc::toc()
## 2.382 sec elapsed

number of clusters —-

tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "wss", n = 1000)

tictoc::toc()
## 2.686 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "silhouette", n = 1000)

tictoc::toc()
## 2.197 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "gap_stat", n = 1000)
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations

tictoc::toc()
## 43.429 sec elapsed

monte carlo permutations —-

tictoc::tic()
chooseGCM::montecarlo_gcms(s, var_names, study_area_parana, perm = 10000, method = "euclidean") 
## $montecarlo_plot

## 
## $suggested_gcms
## $suggested_gcms$k2
## [1] "ml" "uk"
## 
## $suggested_gcms$k3
## [1] "ac" "ae" "ic"
## 
## $suggested_gcms$k4
## [1] "cc" "ch" "cr" "hg"
## 
## $suggested_gcms$k5
## [1] "cr" "mr" "ev" "gg" "ae"
## 
## $suggested_gcms$k6
## [1] "cc" "ic" "ip" "fi" "mi" "ch"
## 
## $suggested_gcms$k7
## [1] "gh" "hg" "me" "mp" "ev" "cn" "ac"
## 
## $suggested_gcms$k8
## [1] "ac" "in" "cr" "ch" "uk" "ev" "mr" "gg"
## 
## $suggested_gcms$k9
## [1] "ce" "gh" "gg" "ic" "hg" "cn" "ac" "mr" "ae"
## 
## $suggested_gcms$k10
##  [1] "gh" "in" "gg" "fi" "ce" "mi" "ch" "cn" "uk" "ev"
## 
## $suggested_gcms$k11
##  [1] "cr" "ev" "gg" "ac" "gh" "ae" "mp" "ec" "ml" "cn" "mr"
## 
## $suggested_gcms$k12
##  [1] "ic" "ml" "ev" "ec" "cn" "mp" "ce" "mi" "in" "ch" "ac" "ae"
## 
## $suggested_gcms$k13
##  [1] "cc" "ic" "ip" "fi" "mi" "ch" "cn" "ml" "in" "hg" "ce" "uk" "ev"
## 
## $suggested_gcms$k14
##  [1] "ce" "ec" "uk" "ic" "cn" "mp" "ml" "in" "ev" "cr" "ac" "ch" "mr" "hg"
## 
## $suggested_gcms$k15
##  [1] "cn" "ip" "cr" "ic" "hg" "mp" "ac" "ml" "mr" "ae" "gh" "uk" "ev" "ch" "ec"
## 
## $suggested_gcms$k16
##  [1] "gh" "ml" "mp" "ce" "in" "ip" "ic" "mi" "cr" "ch" "uk" "hg" "cn" "ev" "ae"
## [16] "ec"
## 
## $suggested_gcms$k17
##  [1] "ch" "ic" "cc" "hg" "ml" "in" "ce" "cr" "cn" "ev" "mp" "ec" "mi" "ac" "ae"
## [16] "mr" "gg"
## 
## $suggested_gcms$k18
##  [1] "gh" "ml" "mp" "ce" "in" "ip" "ic" "mi" "cr" "ch" "uk" "hg" "cn" "ev" "ae"
## [16] "ec" "ac" "fi"
## 
## $suggested_gcms$k19
##  [1] "ce" "mp" "ev" "in" "hg" "ec" "ic" "cr" "cn" "ml" "ch" "ac" "ae" "mr" "fi"
## [16] "gh" "uk" "cc" "me"
## 
## $suggested_gcms$k20
##  [1] "cc" "gg" "mi" "ch" "in" "uk" "cn" "mp" "ce" "hg" "ev" "ec" "ml" "ip" "ic"
## [16] "cr" "ac" "ae" "mr" "fi"
## 
## $suggested_gcms$k21
##  [1] "ce" "ec" "uk" "ic" "cn" "mp" "ml" "in" "ev" "cr" "ac" "ch" "mr" "hg" "ae"
## [16] "gh" "gg" "cc" "me" "mi" "ip"
tictoc::toc()
## 19.535 sec elapsed

environment —-

tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = res5$suggested_gcms$k3) 

tictoc::toc()
## 3.355 sec elapsed
tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = "sum") 

tictoc::toc()
## 3.457 sec elapsed

2.5 min ——————————————————————

import —-

tictoc::tic()
s <- chooseGCM::import_gcms(path = "~/storage/WC_data/WC_data_all_gcms_25")
tictoc::toc()
## 0.581 sec elapsed
s
## $ac_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ac_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,   bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.9, -2.6, -137.8,    6.5, -26.1, -65.4, ... 
## max values  :  38.5, 22.3,   94.6, 2256.7,  56.7,  31.1, ... 
## 
## $ae_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ae_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.6, -1.2, -15.0,    8.8, -26.9, -67.5, ... 
## max values  :  37.1, 22.1,  94.5, 2241.0,  55.9,  29.9, ... 
## 
## $cc_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cc_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -45.1, -2.9, -51.9,    9.0, -25.5, -65.0, ... 
## max values  :  39.9, 22.2,  95.2, 2234.1,  58.6,  32.9, ... 
## 
## $ce_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ce_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.3,   -3, -39.3,    8.1, -26.9, -67.5, ... 
## max values  :  37.4,   23,  95.8, 2293.0,  55.8,  30.0, ... 
## 
## $ch_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ch_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.9, -3.2, -64.5,    9.6, -24.9, -67.6, ... 
## max values  :  38.0, 22.8,  94.7, 2265.5,  58.7,  30.3, ... 
## 
## $cn_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cn_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -3.2, -74.2,   10.0, -25.5, -66.6, ... 
## max values  :  38.0, 23.1,  95.0, 2171.8,  57.1,  30.4, ... 
## 
## $cr_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cr_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.8, -40.5,    7.2, -25.9, -67.8, ... 
## max values  :  37.6, 23.1,  95.7, 2192.1,  56.2,  30.1, ... 
## 
## $ec_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ec_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.7, -3.0, -34.1,    8.5, -26.5, -68.6, ... 
## max values  :  38.1, 22.1,  96.2, 2367.9,  56.0,  30.4, ... 
## 
## $ev_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ev_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.0, -2.7, -18.1,    9.9, -26.9, -67.1, ... 
## max values  :  38.1, 22.1,  96.3, 2366.4,  55.8,  30.3, ... 
## 
## $fi_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : fi_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.4, -2.8, -34.3,    9.1, -25.2, -66.7, ... 
## max values  :  37.1, 22.1,  95.3, 2194.6,  55.2,  29.8, ... 
## 
## $gg_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gg_ssp585_2.5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.1, -0.6, -5.1,    4.7, -27.0, -66.2, ... 
## max values  :  37.0, 22.2, 96.6, 2279.2,  55.2,  29.6, ... 
## 
## $gh_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gh_ssp585_2.5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -0.4, -3.9,    8.1, -27.2, -66.0, ... 
## max values  :  37.4, 22.9, 96.1, 2266.4,  55.8,  29.7, ... 
## 
## $hg_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : hg_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,   bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -2.9, -103.6,    8.4, -25.6, -66.6, ... 
## max values  :  38.7, 21.6,   95.8, 2300.9,  57.2,  32.2, ... 
## 
## $ic_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ic_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.0, -2.9, -45.3,    7.8, -26.9, -69.1, ... 
## max values  :  35.6, 22.8,  96.2, 2332.6,  52.8,  28.7, ... 
## 
## $in_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : in_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.6, -2.2, -28.0,    5.8, -26.3, -67.2, ... 
## max values  :  35.9, 23.4,  95.3, 2350.8,  53.7,  29.2, ... 
## 
## $ip_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ip_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.6, -31.3,    8.6, -26.5, -65.5, ... 
## max values  :  38.1, 22.0,  94.8, 2199.1,  56.5,  31.1, ... 
## 
## $me_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : me_ssp585_2.5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.8, -1.0, -9.3,    7.8, -29.8, -69.2, ... 
## max values  :  36.4, 23.3, 95.0, 2246.0,  54.9,  29.4, ... 
## 
## $mi_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mi_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.4, -1.9, -15.0,    8.3, -28.5, -68.4, ... 
## max values  :  36.6, 23.3,  95.8, 2262.1,  55.4,  28.9, ... 
## 
## $ml_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ml_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.4, -0.9, -14.3,    8.6, -27.6, -68.8, ... 
## max values  :  35.6, 22.0,  96.3, 2257.2,  54.5,  29.4, ... 
## 
## $mp_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mp_ssp585_2.5_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.8, -0.7, -7.7,   10.2, -27.7, -67.1, ... 
## max values  :  35.4, 21.9, 96.4, 2289.6,  53.9,  29.2, ... 
## 
## $mr_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mr_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.7, -1.8, -20.7,    7.5, -26.2, -68.3, ... 
## max values  :  36.7, 22.8,  95.0, 2161.6,  55.4,  29.5, ... 
## 
## $uk_ssp585_2.5_2090
## class       : SpatRaster 
## dimensions  : 4320, 8640, 19  (nrow, ncol, nlyr)
## resolution  : 0.04166667, 0.04166667  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : uk_ssp585_2.5_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.6, -3.7, -73.4,    6.2, -25.7, -66.6, ... 
## max values  :  39.0, 21.7,  95.6, 2330.6,  57.7,  31.7, ...
names(s)
##  [1] "ac_ssp585_2.5_2090" "ae_ssp585_2.5_2090" "cc_ssp585_2.5_2090"
##  [4] "ce_ssp585_2.5_2090" "ch_ssp585_2.5_2090" "cn_ssp585_2.5_2090"
##  [7] "cr_ssp585_2.5_2090" "ec_ssp585_2.5_2090" "ev_ssp585_2.5_2090"
## [10] "fi_ssp585_2.5_2090" "gg_ssp585_2.5_2090" "gh_ssp585_2.5_2090"
## [13] "hg_ssp585_2.5_2090" "ic_ssp585_2.5_2090" "in_ssp585_2.5_2090"
## [16] "ip_ssp585_2.5_2090" "me_ssp585_2.5_2090" "mi_ssp585_2.5_2090"
## [19] "ml_ssp585_2.5_2090" "mp_ssp585_2.5_2090" "mr_ssp585_2.5_2090"
## [22] "uk_ssp585_2.5_2090"
names(s) <- gsub("_ssp585_2.5_2090", "", names(s))
names(s)
##  [1] "ac" "ae" "cc" "ce" "ch" "cn" "cr" "ec" "ev" "fi" "gg" "gh" "hg" "ic" "in"
## [16] "ip" "me" "mi" "ml" "mp" "mr" "uk"

variable names and study area —-

var_names <- c("bio5", "bio13", "bio15")

study_area_parana <- geodata::gadm(country = "Brazil", path = "input_data/") %>% 
  sf::st_as_sf() %>% 
  dplyr::filter(NAME_1 == "Paraná")
study_area_parana
## Simple feature collection with 1 feature and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -54.61602 ymin: -26.71712 xmax: -48.02354 ymax: -22.5163
## Geodetic CRS:  WGS 84
##      GID_1 GID_0 COUNTRY NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1
## 1 BRA.16_1   BRA  Brazil Paraná      <NA>      <NA> Estado     State <NA>
##   HASC_1 ISO_1                       geometry
## 1  BR.PR  <NA> MULTIPOLYGON (((-52.52423 -...
plot(study_area_parana$geometry)

compare —-

tictoc::tic()
res25 <- chooseGCM::compare_gcms(s, var_names, study_area_parana, k = 3) 
tictoc::toc()
## 43.466 sec elapsed
res25$statistics_gcms

summary —-

tictoc::tic()
s_sum <- chooseGCM::summary_gcms(s, var_names, study_area_parana) 
tictoc::toc()
## 6.326 sec elapsed
s_sum
## $ac
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.0          33.1     35  34.81437          36.5  39.2  2.070698   0
## bio13 218.0         283.0    314 324.21609         369.0 453.0 50.222811   0
## bio15  20.6          31.5     36  35.78631          39.9  54.4  6.340011   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ae
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.4          31.7   33.8  33.72196          35.6  39.1  2.417791   0
## bio13 161.0         206.0  236.0 232.93535         255.0 373.0 35.218328   0
## bio15  13.6          23.2   27.0  27.77290          32.3  50.5  6.856069   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $cc
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   30.6          36.9   40.4  40.10014          43.2  46.4  3.528354   0
## bio13 114.0         163.0  176.0 180.27033         192.0 275.0 23.590208   0
## bio15  25.2          36.2   41.4  41.69267          46.9  64.8  7.745826   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ce
##         min quantile_0.25 median     mean quantile_0.75   max        sd NAs
## bio5   26.3          31.0   33.3  33.0884          35.1  37.7  2.377210   0
## bio13 153.0         188.0  208.0 210.9456         228.0 365.0 31.883072   0
## bio15  21.6          30.4   34.5  35.6003          39.6  62.7  7.352477   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ch
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.0          32.8     35  34.73472          36.7  38.5  2.235619   0
## bio13 157.0         198.0    228 234.93781         264.0 368.0 43.146596   0
## bio15  20.8          29.6     34  33.95508          37.4  52.0  5.685894   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $cn
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.6          32.3   34.2  34.00039          35.7  38.1  2.024727   0
## bio13 158.0         202.0  232.0 244.01306         284.0 373.0 50.228169   0
## bio15  21.4          29.1   33.7  33.65463          37.5  51.6  5.615701   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $cr
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.5          32.2   33.9  33.71662          35.3  37.7  1.884897   0
## bio13 149.0         198.0  225.0 230.09210         259.0 346.0 38.733435   0
## bio15  17.4          27.4   32.9  32.96107          37.9  52.1  7.025246   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ec
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.0          30.6   33.3  33.62576          36.4  40.6  3.307145   0
## bio13 149.0         191.0  221.0 223.46361         251.0 364.0 38.608722   0
## bio15  22.5          30.0   32.9  33.58756          37.1  49.3  4.745219   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ev
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.2          31.0   33.9  34.07875          36.9  41.0  3.414492   0
## bio13 142.0         179.0  203.0 208.89266         232.0 391.0 38.153353   0
## bio15  24.1          30.3   33.9  34.49860          37.8  52.4  5.031405   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $fi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.7          31.9   33.8  33.65872          35.4  38.7  2.105936   0
## bio13 150.0         201.0  220.0 226.25812         250.0 368.0 32.421047   0
## bio15  17.5          25.7   29.6  30.43187          33.9  52.6  6.260145   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $gg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.2          30.9   32.8  32.62626          34.4  36.9  2.083460   0
## bio13 136.0         176.0  197.0 197.28339         214.0 346.0 25.685600   0
## bio15  11.3          18.9   22.5  23.80977          27.7  47.2  6.771745   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $gh
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.1          30.7   32.8  32.61633          34.4  36.8  2.196212   0
## bio13 140.0         181.0  209.0 210.02735         238.0 312.0 31.756119   0
## bio15  18.7          25.7   28.1  29.35060          32.7  50.3  5.617285   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $hg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.8          34.5   36.7  36.48552          38.5  41.4  2.387960   0
## bio13 166.0         211.0  237.0 245.20502         278.0 399.0 42.331741   0
## bio15  14.4          23.1   27.0  27.88863          31.8  50.2  6.202554   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ic
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.6          30.8   32.5  32.37363          33.9  36.8  1.902466   0
## bio13 133.0         171.0  187.0 194.15977         214.0 324.0 30.043856   0
## bio15  14.0          24.0   27.8  28.78018          33.1  53.2  6.924022   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $`in`
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.9          30.8   32.5  32.43618          33.9  37.3  1.917950   0
## bio13 138.0         185.0  200.0 203.77832         215.0 401.0 28.110005   0
## bio15  13.4          23.6   27.9  28.78305          33.3  54.4  7.706249   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ip
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.5          32.9   35.8  35.48258          38.0  40.9  2.969116   0
## bio13 138.0         181.0  195.0 197.91065         212.0 323.0 22.638286   0
## bio15  14.9          26.5   32.7  33.33308          39.3  62.8  9.272308   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $me
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   25.9          29.6   31.4  31.29991          32.8  35.7  1.927461   0
## bio13 143.0         179.0  201.0 205.54274         227.0 347.0 30.934144   0
## bio15  15.3          25.9   28.8  29.80895          33.7  52.7  6.507155   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $mi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.5            32   33.1  33.04610          34.2  37.3  1.514641   0
## bio13 153.0           192  210.0 216.62111         241.0 358.0 31.500723   0
## bio15  16.6            26   29.2  29.67656          33.3  46.5  5.452674   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $ml
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.5          30.5   32.3  32.21425          33.8  37.3  1.980003   0
## bio13 133.0         173.0  189.0 190.65405         204.0 369.0 26.120074   0
## bio15  17.6          28.6   33.0  34.16302          39.6  59.0  8.348566   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $mp
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.2          30.3   32.0  31.95781          33.6  36.4  1.952084   0
## bio13 145.0         189.0  208.0 210.68557         230.0 321.0 29.173701   0
## bio15  14.6          25.4   29.4  30.83000          35.8  54.8  7.784779   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $mr
##       min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26          30.4   32.1  31.91638          33.4  36.5  1.844380   0
## bio13 165         212.0  244.0 245.64752         277.0 345.0 38.175787   0
## bio15  18          26.7   32.2  32.08751          37.0  51.6  6.996644   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565
## 
## $uk
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   28.8          34.1   36.3  36.22786          38.3  41.6  2.484018   0
## bio13 154.0         200.0  230.0 235.30185         267.0 412.0 45.061116   0
## bio15  16.6          25.9   30.3  30.72653          35.1  50.4  6.379371   0
##       n_cells
## bio5    10565
## bio13   10565
## bio15   10565

correlation —-

tictoc::tic()
s_cor <- chooseGCM::cor_gcms(s, var_names, study_area_parana, method = "pearson")
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
tictoc::toc()
## 6.173 sec elapsed
s_cor
## $cor_matrix
##           ac        ae        cc        ce        ch        cn        cr
## ac 1.0000000 0.8139634 0.7660266 0.7170416 0.9224718 0.9702233 0.9658966
## ae 0.8139634 1.0000000 0.8721457 0.9141588 0.8959894 0.8602465 0.8847184
## cc 0.7660266 0.8721457 1.0000000 0.8818973 0.8132570 0.7916229 0.8311728
## ce 0.7170416 0.9141588 0.8818973 1.0000000 0.8414982 0.7690865 0.8076441
## ch 0.9224718 0.8959894 0.8132570 0.8414982 1.0000000 0.9444841 0.9612027
## cn 0.9702233 0.8602465 0.7916229 0.7690865 0.9444841 1.0000000 0.9809743
## cr 0.9658966 0.8847184 0.8311728 0.8076441 0.9612027 0.9809743 1.0000000
## ec 0.8292531 0.9127099 0.7912343 0.8150938 0.8982918 0.8695877 0.8737704
## ev 0.8121408 0.9068271 0.8182531 0.8500225 0.9011063 0.8701153 0.8700056
## fi 0.8512930 0.9235382 0.8630133 0.9105928 0.9079849 0.8930941 0.9101423
## gg 0.8255113 0.9479410 0.8702269 0.9320368 0.8963244 0.8676820 0.9068132
## gh 0.9053932 0.8260056 0.7622048 0.7355096 0.8776910 0.9106629 0.9160188
## hg 0.8736136 0.9278481 0.8149989 0.8357167 0.9089380 0.9029207 0.9114124
## ic 0.8915587 0.9219260 0.8745909 0.8892081 0.9436927 0.9213137 0.9509209
## in 0.7126622 0.8917251 0.8681968 0.9075340 0.8100354 0.7630045 0.8097460
## ip 0.6700354 0.9016883 0.9276154 0.9073025 0.7550769 0.7024335 0.7598833
## me 0.8805320 0.9321738 0.8622398 0.8827470 0.9306083 0.9149107 0.9436198
## mi 0.9237415 0.8943422 0.8193965 0.8240080 0.9381709 0.9363030 0.9652732
## ml 0.7669815 0.9319328 0.8768782 0.9393056 0.8510253 0.8092807 0.8550163
## mp 0.7886378 0.9489019 0.8727291 0.9377957 0.8642615 0.8280200 0.8596392
## mr 0.9789484 0.7812845 0.7488343 0.6900854 0.9087668 0.9530417 0.9622767
## uk 0.8569844 0.9400702 0.8357713 0.8505844 0.9155305 0.8929814 0.9082729
##           ec        ev        fi        gg        gh        hg        ic
## ac 0.8292531 0.8121408 0.8512930 0.8255113 0.9053932 0.8736136 0.8915587
## ae 0.9127099 0.9068271 0.9235382 0.9479410 0.8260056 0.9278481 0.9219260
## cc 0.7912343 0.8182531 0.8630133 0.8702269 0.7622048 0.8149989 0.8745909
## ce 0.8150938 0.8500225 0.9105928 0.9320368 0.7355096 0.8357167 0.8892081
## ch 0.8982918 0.9011063 0.9079849 0.8963244 0.8776910 0.9089380 0.9436927
## cn 0.8695877 0.8701153 0.8930941 0.8676820 0.9106629 0.9029207 0.9213137
## cr 0.8737704 0.8700056 0.9101423 0.9068132 0.9160188 0.9114124 0.9509209
## ec 1.0000000 0.9755105 0.8875107 0.8578928 0.8261871 0.9394486 0.8454175
## ev 0.9755105 1.0000000 0.9084734 0.8749150 0.8081205 0.9439760 0.8635806
## fi 0.8875107 0.9084734 1.0000000 0.9525805 0.8665929 0.9516840 0.9307297
## gg 0.8578928 0.8749150 0.9525805 1.0000000 0.8717270 0.9087216 0.9491121
## gh 0.8261871 0.8081205 0.8665929 0.8717270 1.0000000 0.8622663 0.8570566
## hg 0.9394486 0.9439760 0.9516840 0.9087216 0.8622663 1.0000000 0.9025565
## ic 0.8454175 0.8635806 0.9307297 0.9491121 0.8570566 0.9025565 1.0000000
## in 0.7580735 0.7923567 0.8837378 0.9324902 0.7204920 0.8277283 0.9090227
## ip 0.7678969 0.7877979 0.8357978 0.8924127 0.7130811 0.7899364 0.8384474
## me 0.8484223 0.8588014 0.9265864 0.9570321 0.8692828 0.9055871 0.9887543
## mi 0.8619878 0.8632656 0.9155943 0.9228638 0.8683484 0.9173029 0.9638589
## ml 0.8139239 0.8433665 0.9281967 0.9679137 0.7984960 0.8758055 0.9243846
## mp 0.8308353 0.8490195 0.9410524 0.9479968 0.8117982 0.8905020 0.9213531
## mr 0.8012067 0.7859672 0.8377552 0.8186911 0.9272637 0.8486943 0.8796563
## uk 0.9508075 0.9600317 0.9352800 0.9051894 0.8301024 0.9843985 0.9138400
##           in        ip        me        mi        ml        mp        mr
## ac 0.7126622 0.6700354 0.8805320 0.9237415 0.7669815 0.7886378 0.9789484
## ae 0.8917251 0.9016883 0.9321738 0.8943422 0.9319328 0.9489019 0.7812845
## cc 0.8681968 0.9276154 0.8622398 0.8193965 0.8768782 0.8727291 0.7488343
## ce 0.9075340 0.9073025 0.8827470 0.8240080 0.9393056 0.9377957 0.6900854
## ch 0.8100354 0.7550769 0.9306083 0.9381709 0.8510253 0.8642615 0.9087668
## cn 0.7630045 0.7024335 0.9149107 0.9363030 0.8092807 0.8280200 0.9530417
## cr 0.8097460 0.7598833 0.9436198 0.9652732 0.8550163 0.8596392 0.9622767
## ec 0.7580735 0.7678969 0.8484223 0.8619878 0.8139239 0.8308353 0.8012067
## ev 0.7923567 0.7877979 0.8588014 0.8632656 0.8433665 0.8490195 0.7859672
## fi 0.8837378 0.8357978 0.9265864 0.9155943 0.9281967 0.9410524 0.8377552
## gg 0.9324902 0.8924127 0.9570321 0.9228638 0.9679137 0.9479968 0.8186911
## gh 0.7204920 0.7130811 0.8692828 0.8683484 0.7984960 0.8117982 0.9272637
## hg 0.8277283 0.7899364 0.9055871 0.9173029 0.8758055 0.8905020 0.8486943
## ic 0.9090227 0.8384474 0.9887543 0.9638589 0.9243846 0.9213531 0.8796563
## in 1.0000000 0.9020456 0.9125106 0.8707731 0.9583955 0.9097444 0.7038034
## ip 0.9020456 1.0000000 0.8418173 0.7759449 0.9202445 0.9027850 0.6611266
## me 0.9125106 0.8418173 1.0000000 0.9631809 0.9328323 0.9259360 0.8645564
## mi 0.8707731 0.7759449 0.9631809 1.0000000 0.8927420 0.8793438 0.9136859
## ml 0.9583955 0.9202445 0.9328323 0.8927420 1.0000000 0.9686977 0.7598528
## mp 0.9097444 0.9027850 0.9259360 0.8793438 0.9686977 1.0000000 0.7692037
## mr 0.7038034 0.6611266 0.8645564 0.9136859 0.7598528 0.7692037 1.0000000
## uk 0.8358001 0.8108835 0.9159435 0.9171095 0.8818385 0.8932666 0.8283864
##           uk
## ac 0.8569844
## ae 0.9400702
## cc 0.8357713
## ce 0.8505844
## ch 0.9155305
## cn 0.8929814
## cr 0.9082729
## ec 0.9508075
## ev 0.9600317
## fi 0.9352800
## gg 0.9051894
## gh 0.8301024
## hg 0.9843985
## ic 0.9138400
## in 0.8358001
## ip 0.8108835
## me 0.9159435
## mi 0.9171095
## ml 0.8818385
## mp 0.8932666
## mr 0.8283864
## uk 1.0000000
## 
## $cor_plot

distance —-

tictoc::tic()
s_dist <- chooseGCM::dist_gcms(s, var_names, study_area_parana, method = "euclidean")
tictoc::toc()
## 6.117 sec elapsed
s_dist
## $distances
##           ac        ae        cc        ce        ch        cn        cr
## ae 108.58979                                                            
## cc 121.77917  90.02174                                                  
## ce 133.92174  73.76287  86.52065                                        
## ch  70.10028  81.19487 108.79575 100.23213                              
## cn  43.44385  94.11767 114.92507 120.98026  59.31966                    
## cr  46.49308  85.48106 103.44537 110.41868  49.58962  34.72643          
## ec 104.03183  74.38278 115.03220 108.25938  80.29119  90.91783  89.44796
## ev 109.12041  76.84835 107.33055  97.49961  79.17246  90.73374  90.77205
## fi  97.08574  69.61648  93.18137  75.27940  76.36941  82.31720  75.46880
## gg 105.16556  57.44306  90.69474  65.63366  81.06400  91.57971  76.85410
## gh  77.43743 105.01649 122.76974 129.47764  88.04792  75.24988  72.95934
## hg  89.50349  67.62598 108.28716 102.04378  75.97284  78.44283  74.93356
## ic  82.90623  70.34661  89.15676  83.79997  59.74098  70.62190  55.77482
## in 134.95413  82.84259  91.40140  76.55629 109.73019 122.56313 109.81376
## ip 144.61837  78.93916  67.73495  76.65205 124.59618 137.33520 123.36757
## me  87.01931  65.56748  93.44405  86.20883  66.31984  73.43910  59.77963
## mi  69.52388  81.83527 106.99241 105.61759  62.60172  63.54029  46.91612
## ml 121.53042  65.68389  88.33998  62.02463  97.17311 109.94796  95.86265
## mp 115.74534  56.91048  89.81615  62.79138  92.75585 104.40679  94.32193
## mr  36.52858 117.74150 126.17404 140.15573  76.04421  54.55644  48.89839
## uk  95.20979  61.63272 102.02682  97.31680  73.17113  82.36058  76.24980
##           ec        ev        fi        gg        gh        hg        ic
## ae                                                                      
## cc                                                                      
## ce                                                                      
## ch                                                                      
## cn                                                                      
## cr                                                                      
## ec                                                                      
## ev  39.39852                                                            
## fi  84.43946  76.16640                                                  
## gg  94.90693  89.04150  54.82371                                        
## gh 104.96168 110.28186  91.95582  90.16905                              
## hg  61.95153  59.59046  55.33950  76.06307  93.43506                    
## ic  98.98513  92.98822  66.26183  56.79328  95.18573  78.58982          
## in 123.83161 114.72254  85.84383  65.41440 133.10272 104.49531  75.93749
## ip 121.29148 115.97508 102.01858  82.57913 134.85574 115.38921 101.19213
## me  98.01836  94.60301  68.21472  52.18694  91.02407  77.35804  26.69824
## mi  93.52950  93.09551  73.14351  69.92282  91.34880  72.39941  47.86194
## ml 108.60134  99.63963  67.46242  45.09723 113.01384  88.72396  69.23011
## mp 103.54870  97.82509  61.12557  57.41229 109.21987  83.30921  70.60421
## mr 112.25112 116.47426 101.40870 107.20112  67.89931  97.93039  87.33767
## uk  55.83923  50.33238  64.04852  77.52082 103.77278  31.44655  73.89971
##           in        ip        me        mi        ml        mp        mr
## ae                                                                      
## cc                                                                      
## ce                                                                      
## ch                                                                      
## cn                                                                      
## cr                                                                      
## ec                                                                      
## ev                                                                      
## fi                                                                      
## gg                                                                      
## gh                                                                      
## hg                                                                      
## ic                                                                      
## in                                                                      
## ip  78.79557                                                            
## me  74.46765 100.13118                                                  
## mi  90.50367 119.17009  48.30881                                        
## ml  51.35231  71.10008  65.24845  82.45267                              
## mp  75.63573  78.49763  68.51621  87.45096  44.54282                    
## mr 137.01871 146.55768  92.65505  73.96574 123.37541 120.94956          
## uk 102.01788 109.48499  72.99205  72.48403  86.54219  82.25078 104.29552
## 
## $heatmap

k-means —-

tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3, method = "euclidean")
## $suggested_gcms
##    1    2    3 
## "uk" "ml" "cr" 
## 
## $kmeans_plot

tictoc::toc()
## 7.306 sec elapsed
tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "ec" "ic" "mi"
## 
## $kmeans_plot

tictoc::toc()
## 7.053 sec elapsed

hierarchical clustering —-

tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "ch" "ae" "ev"
## 
## $dend_plot

tictoc::toc()
## 7.064 sec elapsed
tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3, n = 1000) 
## $suggested_gcms
## [1] "ch" "ae" "ev"
## 
## $dend_plot

tictoc::toc()
## 6.551 sec elapsed

Closestdist algorithm —-

tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "ec" "gh" "hg"
## 
## $best_mean_diff
## [1] 0.007094148
## 
## $global_mean
## [1] 86.77566
tictoc::toc()
## 6.226 sec elapsed
tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana) 
## $suggested_gcms
## [1] "cn" "gg" "gh" "uk"
## 
## $best_mean_diff
## [1] 0.0001932705
## 
## $global_mean
## [1] 86.77566
tictoc::toc()
## 6.723 sec elapsed

number of clusters —-

tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "wss", n = 1000) 

tictoc::toc()
## 6.065 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "silhouette", n = 1000) 

tictoc::toc()
## 5.986 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "gap_stat", n = 1000) 
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations

tictoc::toc()
## 47.38 sec elapsed

monte carlo permutations —-

tictoc::tic()
chooseGCM::montecarlo_gcms(s, var_names, study_area_parana, perm = 10000, method = "euclidean") 
## $montecarlo_plot

## 
## $suggested_gcms
## $suggested_gcms$k2
## [1] "ml" "uk"
## 
## $suggested_gcms$k3
## [1] "ec" "gh" "hg"
## 
## $suggested_gcms$k4
## [1] "cn" "gg" "gh" "uk"
## 
## $suggested_gcms$k5
## [1] "cc" "ch" "cr" "hg" "ec"
## 
## $suggested_gcms$k6
## [1] "cr" "ev" "gg" "ac" "gh" "ae"
## 
## $suggested_gcms$k7
## [1] "fi" "ic" "mr" "ml" "in" "ce" "ch"
## 
## $suggested_gcms$k8
## [1] "ic" "uk" "cc" "ev" "cn" "mp" "ec" "ml"
## 
## $suggested_gcms$k9
## [1] "fi" "ip" "cc" "ic" "mi" "ch" "cn" "ml" "ce"
## 
## $suggested_gcms$k10
##  [1] "gg" "mp" "gh" "uk" "ev" "cn" "ml" "ce" "ch" "ec"
## 
## $suggested_gcms$k11
##  [1] "gg" "ic" "mr" "in" "cn" "hg" "mp" "ac" "gh" "uk" "ml"
## 
## $suggested_gcms$k12
##  [1] "ae" "cc" "in" "fi" "mi" "ch" "ce" "hg" "ev" "ec" "cn" "mp"
## 
## $suggested_gcms$k13
##  [1] "gg" "ic" "mr" "in" "cn" "hg" "mp" "ac" "gh" "uk" "ml" "ae" "ev"
## 
## $suggested_gcms$k14
##  [1] "gh" "ip" "gg" "ml" "in" "mi" "ce" "cr" "ae" "hg" "ch" "cn" "uk" "ev"
## 
## $suggested_gcms$k15
##  [1] "ac" "cc" "cr" "me" "hg" "mp" "ml" "in" "ch" "cn" "mr" "ae" "ev" "uk" "ec"
## 
## $suggested_gcms$k16
##  [1] "gh" "in" "gg" "fi" "ce" "hg" "cr" "cn" "ac" "ml" "uk" "ev" "ec" "mp" "ch"
## [16] "mr"
## 
## $suggested_gcms$k17
##  [1] "ch" "ic" "cc" "hg" "ml" "ev" "cn" "ec" "mp" "ce" "mi" "in" "cr" "ac" "ae"
## [16] "mr" "fi"
## 
## $suggested_gcms$k18
##  [1] "gh" "ml" "mp" "ce" "in" "ip" "cc" "ic" "mi" "fi" "uk" "cr" "ch" "hg" "cn"
## [16] "ev" "ae" "ec"
## 
## $suggested_gcms$k19
##  [1] "cr" "ic" "ce" "mr" "ae" "hg" "ev" "ml" "ec" "cn" "ac" "mp" "gh" "gg" "ch"
## [16] "in" "uk" "cc" "me"
## 
## $suggested_gcms$k20
##  [1] "ae" "cc" "in" "fi" "mi" "ch" "ce" "hg" "ev" "ec" "cn" "mp" "ml" "ip" "me"
## [16] "cr" "ac" "uk" "mr" "gg"
## 
## $suggested_gcms$k21
##  [1] "cr" "ic" "ce" "mr" "ae" "hg" "ev" "ml" "ec" "cn" "ac" "mp" "gh" "gg" "ch"
## [16] "in" "uk" "cc" "me" "mi" "ip"
tictoc::toc()
## 25.47 sec elapsed

environment —-

tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = res25$suggested_gcms$k3) 

tictoc::toc()
## 9.72 sec elapsed
tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = "sum")

tictoc::toc()
## 9.59 sec elapsed

30 sec ——————————————————————

import —-

tictoc::tic()
s <- chooseGCM::import_gcms(path = "~/storage/WC_data/WC_data_all_gcms_30") 
tictoc::toc()
## 0.854 sec elapsed
s
## $ac_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ac_ssp585_30_2090.tif 
## names       :  bio1, bio2,   bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.0, -4.1, -248.1,    5.4, -26.5, -65.4, ... 
## max values  :  38.5, 22.5,   95.0, 2265.1,  56.8,  31.1, ... 
## 
## $ae_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ae_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.5, -0.9, -17.2,    9.4, -27.1, -67.5, ... 
## max values  :  37.3, 22.4,  94.7, 2243.5,  56.0,  29.9, ... 
## 
## $cc_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cc_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -45.1, -3.0, -51.9,    8.2, -25.9, -65.0, ... 
## max values  :  40.0, 22.5,  95.7, 2236.2,  58.7,  33.1, ... 
## 
## $ce_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ce_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.3, -3.0, -39.4,    8.1, -27.3, -67.5, ... 
## max values  :  37.5, 23.4,  95.8, 2295.4,  56.0,  30.0, ... 
## 
## $ch_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ch_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.9, -3.3, -42.9,    9.5, -25.3, -67.6, ... 
## max values  :  38.1, 23.1,  95.2, 2269.5,  58.8,  30.3, ... 
## 
## $cn_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cn_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -3.2, -30.5,   10.1, -25.9, -66.6, ... 
## max values  :  38.2, 23.4,  95.5, 2175.4,  57.2,  30.4, ... 
## 
## $cr_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : cr_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.8, -24.4,    7.6, -26.3, -67.8, ... 
## max values  :  37.7, 23.4,  96.4, 2195.7,  56.4,  30.2, ... 
## 
## $ec_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ec_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.7, -3.0, -34.1,    8.5, -27.0, -68.6, ... 
## max values  :  38.2, 22.4,  97.0, 2370.9,  56.2,  30.6, ... 
## 
## $ev_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ev_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.0, -2.8, -18.1,    9.0, -27.4, -67.2, ... 
## max values  :  38.2, 22.3,  96.4, 2368.5,  56.0,  30.3, ... 
## 
## $fi_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : fi_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.4, -2.9, -41.7,    8.7, -25.6, -66.8, ... 
## max values  :  37.2, 22.4,  95.6, 2195.8,  55.3,  29.9, ... 
## 
## $gg_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gg_ssp585_30_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.1, -0.6, -5.3,    5.8, -27.4, -66.2, ... 
## max values  :  37.1, 22.6, 97.3, 2282.3,  55.3,  29.7, ... 
## 
## $gh_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : gh_ssp585_30_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -0.4, -5.2,    5.7, -27.7, -66.0, ... 
## max values  :  37.5, 23.1, 96.6, 2268.4,  55.9,  29.7, ... 
## 
## $hg_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : hg_ssp585_30_2090.tif 
## names       :  bio1, bio2,   bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.8, -3.1, -112.8,    7.0, -26.0, -66.7, ... 
## max values  :  38.8, 21.8,   96.3, 2304.8,  57.4,  32.3, ... 
## 
## $ic_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ic_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.0, -3.3, -51.2,    7.7, -27.4, -69.2, ... 
## max values  :  35.8, 23.1,  96.1, 2335.2,  52.9,  28.8, ... 
## 
## $in_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : in_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.6, -2.2, -28.9,    5.4, -26.7, -67.2, ... 
## max values  :  36.0, 23.7,  95.7, 2353.8,  53.8,  29.3, ... 
## 
## $ip_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ip_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -48.5, -2.7, -38.4,    6.8, -26.9, -65.5, ... 
## max values  :  38.3, 22.3,  95.3, 2200.0,  56.9,  31.1, ... 
## 
## $me_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : me_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.8, -1.0, -10.0,    5.6, -30.3, -69.3, ... 
## max values  :  36.4, 23.6,  95.4, 2250.7,  55.1,  29.4, ... 
## 
## $mi_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mi_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.4, -2.0, -15.3,    7.0, -29.0, -68.4, ... 
## max values  :  36.7, 23.6,  95.9, 2264.9,  55.5,  28.9, ... 
## 
## $ml_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : ml_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -50.4, -0.9, -23.0,    8.2, -28.0, -68.8, ... 
## max values  :  35.8, 22.2,  96.7, 2261.7,  54.7,  29.4, ... 
## 
## $mp_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mp_ssp585_30_2090.tif 
## names       :  bio1, bio2, bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.8, -0.7, -7.7,    9.9, -28.1, -67.2, ... 
## max values  :  35.5, 22.2, 96.9, 2292.6,  54.0,  29.3, ... 
## 
## $mr_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : mr_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -49.7, -1.8, -21.3,    7.2, -26.7, -68.3, ... 
## max values  :  36.8, 23.1,  95.8, 2163.8,  55.6,  29.5, ... 
## 
## $uk_ssp585_30_2090
## class       : SpatRaster 
## dimensions  : 21600, 43200, 19  (nrow, ncol, nlyr)
## resolution  : 0.008333333, 0.008333333  (x, y)
## extent      : -180, 180, -90, 90  (xmin, xmax, ymin, ymax)
## coord. ref. : lon/lat WGS 84 (EPSG:4326) 
## source      : uk_ssp585_30_2090.tif 
## names       :  bio1, bio2,  bio3,   bio4,  bio5,  bio6, ... 
## min values  : -47.6, -3.7, -92.3,    6.4, -26.1, -66.6, ... 
## max values  :  39.2, 22.0,  95.9, 2335.8,  57.8,  31.8, ...
names(s)
##  [1] "ac_ssp585_30_2090" "ae_ssp585_30_2090" "cc_ssp585_30_2090"
##  [4] "ce_ssp585_30_2090" "ch_ssp585_30_2090" "cn_ssp585_30_2090"
##  [7] "cr_ssp585_30_2090" "ec_ssp585_30_2090" "ev_ssp585_30_2090"
## [10] "fi_ssp585_30_2090" "gg_ssp585_30_2090" "gh_ssp585_30_2090"
## [13] "hg_ssp585_30_2090" "ic_ssp585_30_2090" "in_ssp585_30_2090"
## [16] "ip_ssp585_30_2090" "me_ssp585_30_2090" "mi_ssp585_30_2090"
## [19] "ml_ssp585_30_2090" "mp_ssp585_30_2090" "mr_ssp585_30_2090"
## [22] "uk_ssp585_30_2090"
names(s) <- gsub("_ssp585_30_2090", "", names(s))
names(s)
##  [1] "ac" "ae" "cc" "ce" "ch" "cn" "cr" "ec" "ev" "fi" "gg" "gh" "hg" "ic" "in"
## [16] "ip" "me" "mi" "ml" "mp" "mr" "uk"

variable names and study area —-

var_names <- c("bio5", "bio13", "bio15")

study_area_parana <- geodata::gadm(country = "Brazil", path = "input_data/") %>% 
  sf::st_as_sf() %>% 
  dplyr::filter(NAME_1 == "Paraná")
study_area_parana
## Simple feature collection with 1 feature and 11 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: -54.61602 ymin: -26.71712 xmax: -48.02354 ymax: -22.5163
## Geodetic CRS:  WGS 84
##      GID_1 GID_0 COUNTRY NAME_1 VARNAME_1 NL_NAME_1 TYPE_1 ENGTYPE_1 CC_1
## 1 BRA.16_1   BRA  Brazil Paraná      <NA>      <NA> Estado     State <NA>
##   HASC_1 ISO_1                       geometry
## 1  BR.PR  <NA> MULTIPOLYGON (((-52.52423 -...
plot(study_area_parana$geometry)

compare —-

tictoc::tic()
res30 <- chooseGCM::compare_gcms(s, var_names, study_area_parana, k = 3) 
tictoc::toc()
## 694.261 sec elapsed
res30$statistics_gcms

summary —-

tictoc::tic()
s_sum <- chooseGCM::summary_gcms(s, var_names, study_area_parana) 
tictoc::toc()
## 164.354 sec elapsed
s_sum
## $ac
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.5          33.0   35.0  34.75674          36.4  39.3  2.077827   0
## bio13 214.0         283.0  314.0 324.63327         369.0 455.0 50.329865   0
## bio15  20.2          31.5   36.1  35.78898          39.9  55.7  6.230651   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ae
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   25.1          31.7   33.9  33.74596          35.6  39.3  2.439592   0
## bio13 157.0         206.0  236.0 232.67113         255.0 379.0 34.781844   0
## bio15  13.4          23.2   27.0  27.73133          32.2  51.7  6.729268   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $cc
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   29.2          36.9   40.4  40.09979          43.2  46.7  3.516034   0
## bio13 107.0         163.0  176.0 180.22665         192.0 278.0 23.504125   0
## bio15  24.4          36.3   41.4  41.67614          46.9  66.1  7.633916   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ce
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.6          31.0   33.3  33.08079          35.1  37.8  2.374806   0
## bio13 152.0         188.0  207.0 210.46166         228.0 370.0 31.188642   0
## bio15  21.2          30.4   34.5  35.54252          39.5  63.9  7.192804   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ch
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.6          32.8     35  34.72802          36.6  38.7  2.239152   0
## bio13 154.0         198.0    228 234.55525         264.0 376.0 42.739476   0
## bio15  20.4          29.6     34  33.91930          37.4  53.2  5.585811   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $cn
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.2          32.3   34.2  33.99002          35.7  38.3  2.027334   0
## bio13 153.0         203.0  232.0 243.98954         284.0 380.0 50.099918   0
## bio15  21.2          29.1   33.6  33.61266          37.5  53.0  5.524555   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $cr
##       min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26          32.2   33.9  33.70654          35.3  37.9  1.889443   0
## bio13 143         198.0  225.0 230.05778         259.0 352.0 38.571128   0
## bio15  17          27.4   32.9  32.93161          37.9  53.3  6.919618   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ec
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.4          30.6   33.4  33.61642          36.4  40.7  3.299915   0
## bio13 148.0         191.0  221.0 223.14491         250.0 371.0 38.246157   0
## bio15  21.8          30.0   32.8  33.54136          37.1  50.3  4.667012   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ev
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.5          31.0   34.0  34.07207          36.9  41.1  3.405777   0
## bio13 140.0         179.0  203.0 208.39762         231.0 398.0 37.525993   0
## bio15  23.9          30.3   33.9  34.41288          37.7  53.6  4.930344   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $fi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   25.2          31.9   33.8  33.64941          35.3  38.9  2.108811   0
## bio13 144.0         201.0  220.0 225.98815         250.0 374.0 32.167207   0
## bio15  16.9          25.7   29.6  30.37168          33.8  53.7  6.142075   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $gg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.7          30.8   32.8  32.61777          34.4  37.1  2.087161   0
## bio13 131.0         176.0  197.0 197.05508         214.0 352.0 25.274637   0
## bio15  11.1          18.9   22.5  23.73213          27.6  48.6  6.627553   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $gh
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.5          30.7   32.8  32.60603          34.4  37.0  2.199312   0
## bio13 135.0         181.0  209.0 210.06508         238.0 316.0 31.749807   0
## bio15  17.7          25.7   28.2  29.31666          32.7  50.5  5.516647   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $hg
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.4          34.5   36.7  36.47726          38.5  41.6  2.381972   0
## bio13 165.0         211.0  238.0 245.02011         277.0 406.0 42.150030   0
## bio15  13.8          23.1   26.9  27.81881          31.7  51.2  6.079749   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ic
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   25.1          30.8   32.5  32.35828          33.9  36.9  1.906742   0
## bio13 129.0         171.0  187.0 193.92037         214.0 329.0 29.564723   0
## bio15  13.8          24.1   27.8  28.71757          32.9  54.2  6.770720   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $`in`
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   25.5          30.8   32.5  32.41968          33.9  37.5  1.923271   0
## bio13 127.0         185.0  200.0 203.36371         215.0 408.0 27.091040   0
## bio15  13.3          23.6   27.9  28.69262          33.1  55.8  7.541156   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ip
##         min quantile_0.25 median      mean quantile_0.75 max        sd NAs
## bio5   26.0          32.9   35.8  35.47902          37.9  41  2.961617   0
## bio13 129.0         181.0  195.0 197.77569         212.0 327 22.387017   0
## bio15  14.2          26.6   32.7  33.27870          39.3  64  9.125891   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $me
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.4          29.6   31.4  31.28042          32.8  35.8  1.934235   0
## bio13 138.0         179.0  201.0 205.36992         227.0 353.0 30.548244   0
## bio15  15.1          26.0   28.8  29.75098          33.5  53.8  6.366499   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $mi
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   26.1          32.0   33.1  33.03503          34.2  37.5  1.520775   0
## bio13 148.0         192.0  210.0 216.55162         241.0 364.0 31.172976   0
## bio15  16.3          26.1   29.1  29.63360          33.2  47.9  5.356427   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $ml
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   25.0          30.5   32.3  32.19734          33.8  37.4  1.983525   0
## bio13 125.0         173.0  189.0 190.29838         204.0 376.0 25.444389   0
## bio15  17.2          28.6   32.9  34.07315          39.4  60.2  8.195698   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $mp
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.7          30.3   32.0  31.94418          33.5  36.6  1.959191   0
## bio13 143.0         189.0  208.0 210.45254         230.0 323.0 28.908881   0
## bio15  14.3          25.4   29.3  30.76926          35.6  55.9  7.650058   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $mr
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   24.4          30.4   32.1  31.90439          33.4  36.7  1.848709   0
## bio13 161.0         213.0  244.0 245.84422         277.0 351.0 38.197486   0
## bio15  17.5          26.7   32.2  32.07687          36.9  52.7  6.886176   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025
## 
## $uk
##         min quantile_0.25 median      mean quantile_0.75   max        sd NAs
## bio5   27.4          34.1   36.3  36.21627          38.2  41.7  2.476925   0
## bio13 153.0         200.0  230.0 235.05300         267.0 419.0 44.655216   0
## bio15  16.0          25.9   30.2  30.65224          35.0  51.5  6.264661   0
##       n_cells
## bio5   257025
## bio13  257025
## bio15  257025

correlation —-

tictoc::tic()
s_cor <- chooseGCM::cor_gcms(s, var_names, study_area_parana, method = "pearson") 
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
tictoc::toc()
## 155.123 sec elapsed
s_cor
## $cor_matrix
##           ac        ae        cc        ce        ch        cn        cr
## ac 1.0000000 0.8122169 0.7631950 0.7181001 0.9234464 0.9709334 0.9663427
## ae 0.8122169 1.0000000 0.8703251 0.9121158 0.8923309 0.8572799 0.8818220
## cc 0.7631950 0.8703251 1.0000000 0.8811335 0.8100562 0.7888204 0.8286444
## ce 0.7181001 0.9121158 0.8811335 1.0000000 0.8398084 0.7681467 0.8067192
## ch 0.9234464 0.8923309 0.8100562 0.8398084 1.0000000 0.9434662 0.9604242
## cn 0.9709334 0.8572799 0.7888204 0.7681467 0.9434662 1.0000000 0.9806253
## cr 0.9663427 0.8818220 0.8286444 0.8067192 0.9604242 0.9806253 1.0000000
## ec 0.8299194 0.9119940 0.7879480 0.8122462 0.8959176 0.8662008 0.8715415
## ev 0.8148219 0.9058234 0.8159014 0.8470654 0.8995525 0.8682990 0.8689294
## fi 0.8517183 0.9200224 0.8610767 0.9106371 0.9065536 0.8919529 0.9093529
## gg 0.8274711 0.9459869 0.8687243 0.9303050 0.8948594 0.8678876 0.9071590
## gh 0.9047391 0.8238235 0.7589175 0.7346299 0.8766590 0.9095123 0.9152722
## hg 0.8733453 0.9243747 0.8109585 0.8332653 0.9074649 0.9008316 0.9102628
## ic 0.8946344 0.9194481 0.8734300 0.8871011 0.9437644 0.9226586 0.9520516
## in 0.7163084 0.8901462 0.8716802 0.9063986 0.8097551 0.7650386 0.8114641
## ip 0.6678120 0.8998447 0.9267088 0.9042132 0.7502500 0.6992203 0.7572218
## me 0.8828578 0.9302295 0.8610349 0.8811572 0.9300722 0.9153716 0.9441448
## mi 0.9258046 0.8907080 0.8161957 0.8212276 0.9375651 0.9364120 0.9655690
## ml 0.7698211 0.9304987 0.8780415 0.9377515 0.8499866 0.8097391 0.8557565
## mp 0.7884819 0.9466662 0.8722286 0.9368151 0.8618343 0.8261087 0.8582088
## mr 0.9787688 0.7783074 0.7462309 0.6897783 0.9087588 0.9530833 0.9623545
## uk 0.8574360 0.9377628 0.8322850 0.8480601 0.9141493 0.8905188 0.9069894
##           ec        ev        fi        gg        gh        hg        ic
## ac 0.8299194 0.8148219 0.8517183 0.8274711 0.9047391 0.8733453 0.8946344
## ae 0.9119940 0.9058234 0.9200224 0.9459869 0.8238235 0.9243747 0.9194481
## cc 0.7879480 0.8159014 0.8610767 0.8687243 0.7589175 0.8109585 0.8734300
## ce 0.8122462 0.8470654 0.9106371 0.9303050 0.7346299 0.8332653 0.8871011
## ch 0.8959176 0.8995525 0.9065536 0.8948594 0.8766590 0.9074649 0.9437644
## cn 0.8662008 0.8682990 0.8919529 0.8678876 0.9095123 0.9008316 0.9226586
## cr 0.8715415 0.8689294 0.9093529 0.9071590 0.9152722 0.9102628 0.9520516
## ec 1.0000000 0.9753724 0.8864028 0.8561723 0.8238840 0.9396579 0.8433270
## ev 0.9753724 1.0000000 0.9078687 0.8725271 0.8063192 0.9446322 0.8615965
## fi 0.8864028 0.9078687 1.0000000 0.9519691 0.8644760 0.9505620 0.9298323
## gg 0.8561723 0.8725271 0.9519691 1.0000000 0.8734460 0.9072493 0.9474428
## gh 0.8238840 0.8063192 0.8644760 0.8734460 1.0000000 0.8600696 0.8580527
## hg 0.9396579 0.9446322 0.9505620 0.9072493 0.8600696 1.0000000 0.9013030
## ic 0.8433270 0.8615965 0.9298323 0.9474428 0.8580527 0.9013030 1.0000000
## in 0.7574501 0.7909861 0.8841298 0.9314788 0.7230154 0.8271817 0.9068264
## ip 0.7639798 0.7832689 0.8325297 0.8903918 0.7118162 0.7852168 0.8350921
## me 0.8461826 0.8567366 0.9251953 0.9563779 0.8702427 0.9040857 0.9885066
## mi 0.8612079 0.8627164 0.9144137 0.9212905 0.8677590 0.9172838 0.9630508
## ml 0.8124197 0.8409222 0.9274384 0.9673255 0.8009331 0.8742053 0.9225953
## mp 0.8273344 0.8456542 0.9392771 0.9467776 0.8106824 0.8873043 0.9201154
## mr 0.8001243 0.7863723 0.8373880 0.8197287 0.9266135 0.8483624 0.8813570
## uk 0.9507879 0.9603673 0.9340114 0.9030902 0.8280109 0.9842943 0.9122564
##           in        ip        me        mi        ml        mp        mr
## ac 0.7163084 0.6678120 0.8828578 0.9258046 0.7698211 0.7884819 0.9787688
## ae 0.8901462 0.8998447 0.9302295 0.8907080 0.9304987 0.9466662 0.7783074
## cc 0.8716802 0.9267088 0.8610349 0.8161957 0.8780415 0.8722286 0.7462309
## ce 0.9063986 0.9042132 0.8811572 0.8212276 0.9377515 0.9368151 0.6897783
## ch 0.8097551 0.7502500 0.9300722 0.9375651 0.8499866 0.8618343 0.9087588
## cn 0.7650386 0.6992203 0.9153716 0.9364120 0.8097391 0.8261087 0.9530833
## cr 0.8114641 0.7572218 0.9441448 0.9655690 0.8557565 0.8582088 0.9623545
## ec 0.7574501 0.7639798 0.8461826 0.8612079 0.8124197 0.8273344 0.8001243
## ev 0.7909861 0.7832689 0.8567366 0.8627164 0.8409222 0.8456542 0.7863723
## fi 0.8841298 0.8325297 0.9251953 0.9144137 0.9274384 0.9392771 0.8373880
## gg 0.9314788 0.8903918 0.9563779 0.9212905 0.9673255 0.9467776 0.8197287
## gh 0.7230154 0.7118162 0.8702427 0.8677590 0.8009331 0.8106824 0.9266135
## hg 0.8271817 0.7852168 0.9040857 0.9172838 0.8742053 0.8873043 0.8483624
## ic 0.9068264 0.8350921 0.9885066 0.9630508 0.9225953 0.9201154 0.8813570
## in 1.0000000 0.9024135 0.9116993 0.8688565 0.9571063 0.9099998 0.7056564
## ip 0.9024135 1.0000000 0.8397574 0.7710357 0.9201391 0.9019321 0.6589804
## me 0.9116993 0.8397574 1.0000000 0.9621414 0.9321517 0.9245181 0.8652810
## mi 0.8688565 0.7710357 0.9621414 1.0000000 0.8904440 0.8763561 0.9144050
## ml 0.9571063 0.9201391 0.9321517 0.8904440 1.0000000 0.9688209 0.7613468
## mp 0.9099998 0.9019321 0.9245181 0.8763561 0.9688209 1.0000000 0.7681270
## mr 0.7056564 0.6589804 0.8652810 0.9144050 0.7613468 0.7681270 1.0000000
## uk 0.8343619 0.8064377 0.9141814 0.9166972 0.8798548 0.8900605 0.8279842
##           uk
## ac 0.8574360
## ae 0.9377628
## cc 0.8322850
## ce 0.8480601
## ch 0.9141493
## cn 0.8905188
## cr 0.9069894
## ec 0.9507879
## ev 0.9603673
## fi 0.9340114
## gg 0.9030902
## gh 0.8280109
## hg 0.9842943
## ic 0.9122564
## in 0.8343619
## ip 0.8064377
## me 0.9141814
## mi 0.9166972
## ml 0.8798548
## mp 0.8900605
## mr 0.8279842
## uk 1.0000000
## 
## $cor_plot

distance —-

tictoc::tic()
s_dist <- chooseGCM::dist_gcms(s, var_names, study_area_parana, method = "euclidean") 
tictoc::toc()
## 160.338 sec elapsed
s_dist
## $distances
##          ac       ae       cc       ce       ch       cn       cr       ec
## ae 538.1343                                                               
## cc 604.3074 447.1884                                                      
## ce 659.3407 368.1440 428.1462                                             
## ch 343.5937 407.4816 541.2215 497.0297                                    
## cn 211.7188 469.1428 570.6745 597.9558 295.2681                           
## cr 227.8255 426.9046 514.0575 545.9550 247.0457 172.8542                  
## ec 512.1413 368.3992 571.8520 538.0923 400.6371 454.2440 445.0859         
## ev 534.3887 381.0957 532.8288 485.6411 393.5791 450.6683 449.5884 194.8830
## fi 478.1964 351.1936 462.8604 371.2283 379.6154 408.1962 373.8861 418.5490
## gg 515.8143 288.6104 449.9400 327.8409 402.6686 451.3716 378.3836 470.9597
## gh 383.2831 521.2385 609.7409 639.7178 436.1303 373.5573 361.4727 521.1489
## hg 441.9500 341.5042 539.9344 507.0789 377.7598 391.0651 372.0050 305.0511
## ic 403.0992 352.4522 441.8023 417.2605 294.4884 345.3571 271.9252 491.5408
## in 661.4328 411.5948 444.8457 379.9300 541.6502 601.9504 539.2119 611.5937
## ip 715.7385 393.0062 336.1927 384.3397 620.6049 681.0621 611.8815 603.3051
## me 425.0296 328.0185 462.9299 428.1036 328.3881 361.2606 293.4905 487.0406
## mi 338.2603 410.5411 532.4027 525.0646 310.2961 313.1484 230.4291 462.6417
## ml 595.7928 327.3852 433.6791 309.8324 480.9804 541.6730 471.6400 537.8438
## mp 571.1316 286.7898 443.8939 312.1541 461.5966 517.8469 467.6135 516.0187
## mr 180.9464 584.7067 625.5785 691.6694 375.1095 268.9839 240.9455 555.1911
## uk 468.8861 309.8044 508.5673 484.0591 363.8601 410.8963 378.7291 275.4852
##          ev       fi       gg       gh       hg       ic       in       ip
## ae                                                                        
## cc                                                                        
## ce                                                                        
## ch                                                                        
## cn                                                                        
## cr                                                                        
## ec                                                                        
## ev                                                                        
## fi 376.9346                                                               
## gg 443.3753 272.1590                                                      
## gh 546.5196 457.1625 441.7743                                             
## hg 292.2073 276.1169 378.1996 464.5351                                    
## ic 461.9936 328.9509 284.6940 467.8709 390.1345                           
## in 567.7408 422.7157 325.0685 653.5672 516.2468 379.0608                  
## ip 578.1268 508.1962 411.1346 666.6490 575.5229 504.2933 387.9335         
## me 470.0349 339.6464 259.3677 447.3304 384.5955 133.1332 369.0154 497.1088
## mi 460.1207 363.2993 348.3982 451.5913 357.1558 238.7069 449.7135 594.2188
## ml 495.2987 334.5153 224.4746 554.0666 440.4470 345.4985 257.1930 350.9372
## mp 487.8765 306.0122 286.4902 540.3286 416.8849 350.9895 372.5498 388.8892
## mr 573.9728 500.7706 527.2612 336.4114 483.5773 427.7436 673.7360 725.1905
## uk 247.2232 319.0047 386.5862 515.0068 155.6292 367.8494 505.4086 546.3524
##          me       mi       ml       mp       mr
## ae                                             
## cc                                             
## ce                                             
## ch                                             
## cn                                             
## cr                                             
## ec                                             
## ev                                             
## fi                                             
## gg                                             
## gh                                             
## hg                                             
## ic                                             
## in                                             
## ip                                             
## me                                             
## mi 241.6267                                    
## ml 323.4684 411.0367                           
## mp 341.1802 436.6653 219.2776                  
## mr 455.8026 363.3178 606.6611 597.9813         
## uk 363.7921 358.4201 430.4431 411.7554 515.0468
## 
## $heatmap

k-means —-

tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3, method = "euclidean") 
## $suggested_gcms
##    1    2    3 
## "uk" "cr" "ml" 
## 
## $kmeans_plot

tictoc::toc()
## 160.818 sec elapsed
tictoc::tic()
chooseGCM::kmeans_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "ic" "ch" "ev"
## 
## $kmeans_plot

tictoc::toc()
## 157.689 sec elapsed

hierarchical clustering —-

tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "cr" "ae" "hg"
## 
## $dend_plot

tictoc::toc()
## 160.955 sec elapsed
tictoc::tic()
chooseGCM::hclust_gcms(s, var_names, study_area_parana, k = 3, n = 1000) 
## $suggested_gcms
## [1] "cr" "ae" "hg"
## 
## $dend_plot

tictoc::toc()
## 157.692 sec elapsed

Closestdist algorithm —-

tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana, k = 3) 
## $suggested_gcms
## [1] "ce" "ch" "cr"
## 
## $best_mean_diff
## [1] 0.009958008
## 
## $global_mean
## [1] 430.0201
tictoc::toc()
## 157.717 sec elapsed
tictoc::tic()
chooseGCM::closestdist_gcms(s, var_names, study_area_parana) 
## $suggested_gcms
## [1] "ip" "mp" "ic" "uk" "cr"
## 
## $best_mean_diff
## [1] 0.007743907
## 
## $global_mean
## [1] 430.0201
tictoc::toc()
## 158.426 sec elapsed

number of clusters —-

tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "wss", n = 1000) 

tictoc::toc()
## 147.981 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "silhouette", n = 1000) 

tictoc::toc()
## 146.827 sec elapsed
tictoc::tic()
chooseGCM::optk_gcms(s, var_names, study_area_parana, cluster = "kmeans", method = "gap_stat", n = 1000) 
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations
## Warning: did not converge in 10 iterations

tictoc::toc()
## 187.041 sec elapsed

monte carlo permutations —-

tictoc::tic()
chooseGCM::montecarlo_gcms(s, var_names, study_area_parana, perm = 10000, method = "euclidean") 
## $montecarlo_plot

## 
## $suggested_gcms
## $suggested_gcms$k2
## [1] "ml" "uk"
## 
## $suggested_gcms$k3
## [1] "ce" "ch" "cr"
## 
## $suggested_gcms$k4
## [1] "ic" "mr" "hg" "ae"
## 
## $suggested_gcms$k5
## [1] "ip" "mp" "ic" "uk" "cr"
## 
## $suggested_gcms$k6
## [1] "fi" "ip" "cc" "ic" "mi" "hg"
## 
## $suggested_gcms$k7
## [1] "ac" "in" "cr" "ch" "uk" "ev" "mr"
## 
## $suggested_gcms$k8
## [1] "cc" "mp" "in" "mi" "ce" "ip" "fi" "hg"
## 
## $suggested_gcms$k9
## [1] "ev" "ip" "ae" "fi" "ic" "cc" "mi" "ce" "ch"
## 
## $suggested_gcms$k10
##  [1] "ae" "gg" "gh" "ce" "cr" "uk" "ec" "cn" "ac" "mp"
## 
## $suggested_gcms$k11
##  [1] "ac" "in" "cr" "ch" "uk" "ev" "mr" "ec" "gg" "ae" "mp"
## 
## $suggested_gcms$k12
##  [1] "ce" "mp" "ev" "in" "hg" "ec" "ic" "cr" "cn" "ml" "ac" "ch"
## 
## $suggested_gcms$k13
##  [1] "gg" "ip" "uk" "in" "cc" "hg" "ce" "ev" "ic" "ch" "cr" "ec" "mp"
## 
## $suggested_gcms$k14
##  [1] "ip" "ml" "uk" "cc" "hg" "ev" "ce" "me" "mi" "in" "cr" "ch" "cn" "mp"
## 
## $suggested_gcms$k15
##  [1] "ev" "ip" "ae" "fi" "ic" "cc" "mi" "ce" "ch" "in" "hg" "cr" "cn" "ec" "mp"
## 
## $suggested_gcms$k16
##  [1] "ce" "mr" "cr" "ic" "mp" "uk" "ac" "ml" "ev" "ae" "ec" "cn" "gh" "gg" "in"
## [16] "fi"
## 
## $suggested_gcms$k17
##  [1] "cc" "ch" "cr" "ae" "ac" "mp" "hg" "ev" "ml" "ec" "cn" "mr" "gg" "gh" "mi"
## [16] "ce" "fi"
## 
## $suggested_gcms$k18
##  [1] "gh" "ip" "gg" "ml" "ce" "cc" "ic" "mi" "cr" "ch" "uk" "cn" "hg" "ev" "mp"
## [16] "ec" "ae" "in"
## 
## $suggested_gcms$k19
##  [1] "ce" "ec" "uk" "me" "cr" "ac" "ev" "mp" "ml" "cn" "mr" "ae" "gh" "fi" "ch"
## [16] "in" "hg" "cc" "ic"
## 
## $suggested_gcms$k20
##  [1] "cc" "ip" "me" "fi" "mi" "uk" "in" "ce" "cr" "ch" "cn" "mp" "ev" "hg" "ec"
## [16] "ml" "ac" "ae" "mr" "gg"
## 
## $suggested_gcms$k21
##  [1] "ac" "ce" "cr" "me" "hg" "ml" "mr" "mp" "gh" "ch" "ae" "ev" "cn" "ec" "gg"
## [16] "in" "uk" "cc" "ic" "mi" "ip"
tictoc::toc()
## 279.295 sec elapsed

environment —-

tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = res30$suggested_gcms$k3) 

tictoc::toc()
## 216.463 sec elapsed
tictoc::tic()
chooseGCM::env_gcms(s, var_names, study_area_parana, highlight = "sum") 

tictoc::toc()
## 212.203 sec elapsed

end ———————————————————————